{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "C_kKf4TT4I_1"
   },
   "source": [
    "Data Science Case Study\n",
    "\n",
    "This notebook contains a series of exercises designed to explore a range of data science, python scripting, and quantitative reasoning skills. You can, in principle. solve these exercises using a number of different programming languages/environments, but it will likely be easiest for you to simply fill out this notebook with your solutions in the relevant sections following each exercise.\n",
    "\n",
    "The data used in this case study come from the following academic work: https://www.sciencedirect.com/science/article/pii/S2352340918315191\n",
    "\n",
    "Feel free to peruse that paper in order to familiarize yourself with the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "6OF34xrAi19w"
   },
   "source": [
    "## Part 1: A First Look at the Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "jFdAeNfoi19x"
   },
   "source": [
    "### Exercise 1\n",
    "\n",
    "The data are in two separate csv files in this directory. Bring them into this notebook as either a table or dataframe. Do the two datasets have the same column names (features)? If they do, combine the data into a single table or dataframe to work with for the rest of the notebook. How large is the dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "jqqm4U-t8od-"
   },
   "outputs": [],
   "source": [
    "import pandas as pd  # import pandas (dataframe) library for data visulization and manipultaion\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 84
    },
    "colab_type": "code",
    "id": "nsc0Tfnw8rvD",
    "outputId": "f48e6504-1cdd-4dc2-8aad-2e87b983da28"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Archive:  data.zip\n",
      "  inflating: H1.csv                  \n",
      "replace __MACOSX/._H1.csv? [y]es, [n]o, [A]ll, [N]one, [r]ename: N\n",
      "  inflating: H2.csv                  \n"
     ]
    }
   ],
   "source": [
    "os.chdir(\"/content\") # unzip the data.zip file it contain h1.csv and h2.csv\n",
    "!unzip \"data.zip\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 392
    },
    "colab_type": "code",
    "id": "DTkpvYrK8ryg",
    "outputId": "627092ad-5fd7-4c1f-aaaa-2f7426cb173f"
   },
   "outputs": [
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       "      <td>NULL</td>\n",
       "      <td>NULL</td>\n",
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       "      <td>0</td>\n",
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       "      <td>2015-07-03</td>\n",
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       "    <tr>\n",
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       "      <td>2015</td>\n",
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       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>2</td>\n",
       "      <td>0</td>\n",
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       "      <td>Transient</td>\n",
       "      <td>103.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>2015-07-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
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       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
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       "      <td>2</td>\n",
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       "      <td>2015-05-06</td>\n",
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       "      <td>2</td>\n",
       "      <td>0</td>\n",
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       "      <td>15</td>\n",
       "      <td>NULL</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>105.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Canceled</td>\n",
       "      <td>2015-04-22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   IsCanceled  LeadTime  ArrivalDateYear ArrivalDateMonth  \\\n",
       "0           0       342             2015             July   \n",
       "1           0       737             2015             July   \n",
       "2           0         7             2015             July   \n",
       "3           0        13             2015             July   \n",
       "4           0        14             2015             July   \n",
       "5           0        14             2015             July   \n",
       "6           0         0             2015             July   \n",
       "7           0         9             2015             July   \n",
       "8           1        85             2015             July   \n",
       "9           1        75             2015             July   \n",
       "\n",
       "   ArrivalDateWeekNumber  ArrivalDateDayOfMonth  StaysInWeekendNights  \\\n",
       "0                     27                      1                     0   \n",
       "1                     27                      1                     0   \n",
       "2                     27                      1                     0   \n",
       "3                     27                      1                     0   \n",
       "4                     27                      1                     0   \n",
       "5                     27                      1                     0   \n",
       "6                     27                      1                     0   \n",
       "7                     27                      1                     0   \n",
       "8                     27                      1                     0   \n",
       "9                     27                      1                     0   \n",
       "\n",
       "   StaysInWeekNights  Adults  Children  ...      DepositType        Agent  \\\n",
       "0                  0       2         0  ...  No Deposit              NULL   \n",
       "1                  0       2         0  ...  No Deposit              NULL   \n",
       "2                  1       1         0  ...  No Deposit              NULL   \n",
       "3                  1       1         0  ...  No Deposit               304   \n",
       "4                  2       2         0  ...  No Deposit               240   \n",
       "5                  2       2         0  ...  No Deposit               240   \n",
       "6                  2       2         0  ...  No Deposit              NULL   \n",
       "7                  2       2         0  ...  No Deposit               303   \n",
       "8                  3       2         0  ...  No Deposit               240   \n",
       "9                  3       2         0  ...  No Deposit                15   \n",
       "\n",
       "       Company DaysInWaitingList CustomerType    ADR  \\\n",
       "0         NULL                 0    Transient    0.0   \n",
       "1         NULL                 0    Transient    0.0   \n",
       "2         NULL                 0    Transient   75.0   \n",
       "3         NULL                 0    Transient   75.0   \n",
       "4         NULL                 0    Transient   98.0   \n",
       "5         NULL                 0    Transient   98.0   \n",
       "6         NULL                 0    Transient  107.0   \n",
       "7         NULL                 0    Transient  103.0   \n",
       "8         NULL                 0    Transient   82.0   \n",
       "9         NULL                 0    Transient  105.5   \n",
       "\n",
       "   RequiredCarParkingSpaces  TotalOfSpecialRequests ReservationStatus  \\\n",
       "0                         0                       0         Check-Out   \n",
       "1                         0                       0         Check-Out   \n",
       "2                         0                       0         Check-Out   \n",
       "3                         0                       0         Check-Out   \n",
       "4                         0                       1         Check-Out   \n",
       "5                         0                       1         Check-Out   \n",
       "6                         0                       0         Check-Out   \n",
       "7                         0                       1         Check-Out   \n",
       "8                         0                       1          Canceled   \n",
       "9                         0                       0          Canceled   \n",
       "\n",
       "  ReservationStatusDate  \n",
       "0            2015-07-01  \n",
       "1            2015-07-01  \n",
       "2            2015-07-02  \n",
       "3            2015-07-02  \n",
       "4            2015-07-03  \n",
       "5            2015-07-03  \n",
       "6            2015-07-03  \n",
       "7            2015-07-03  \n",
       "8            2015-05-06  \n",
       "9            2015-04-22  \n",
       "\n",
       "[10 rows x 31 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "H1_dataset=pd.read_csv('H1.csv') # Reading the h1 csv\n",
    "H1_dataset.head(10) # Only showing first 10 rows of csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 409
    },
    "colab_type": "code",
    "id": "AAmclFZL8rsN",
    "outputId": "8baee765-2494-4306-f276-68e529f37daa"
   },
   "outputs": [
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       "      <td>9</td>\n",
       "      <td>NULL</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>68.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Canceled</td>\n",
       "      <td>2015-04-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>92</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>9</td>\n",
       "      <td>NULL</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>76.50</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Canceled</td>\n",
       "      <td>2015-06-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>9</td>\n",
       "      <td>NULL</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>76.50</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Canceled</td>\n",
       "      <td>2015-04-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>79</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>9</td>\n",
       "      <td>NULL</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>76.50</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Canceled</td>\n",
       "      <td>2015-06-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>1</td>\n",
       "      <td>NULL</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient-Party</td>\n",
       "      <td>58.67</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>2015-07-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>63</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>9</td>\n",
       "      <td>NULL</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>68.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Canceled</td>\n",
       "      <td>2015-06-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>62</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>8</td>\n",
       "      <td>NULL</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>76.50</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>No-Show</td>\n",
       "      <td>2015-07-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>62</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>8</td>\n",
       "      <td>NULL</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>76.50</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>No-Show</td>\n",
       "      <td>2015-07-02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   IsCanceled  LeadTime  ArrivalDateYear ArrivalDateMonth  \\\n",
       "0           0         6             2015             July   \n",
       "1           1        88             2015             July   \n",
       "2           1        65             2015             July   \n",
       "3           1        92             2015             July   \n",
       "4           1       100             2015             July   \n",
       "5           1        79             2015             July   \n",
       "6           0         3             2015             July   \n",
       "7           1        63             2015             July   \n",
       "8           1        62             2015             July   \n",
       "9           1        62             2015             July   \n",
       "\n",
       "   ArrivalDateWeekNumber  ArrivalDateDayOfMonth  StaysInWeekendNights  \\\n",
       "0                     27                      1                     0   \n",
       "1                     27                      1                     0   \n",
       "2                     27                      1                     0   \n",
       "3                     27                      1                     2   \n",
       "4                     27                      2                     0   \n",
       "5                     27                      2                     0   \n",
       "6                     27                      2                     0   \n",
       "7                     27                      2                     1   \n",
       "8                     27                      2                     2   \n",
       "9                     27                      2                     2   \n",
       "\n",
       "   StaysInWeekNights  Adults  Children  ...      DepositType        Agent  \\\n",
       "0                  2       1       0.0  ...  No Deposit                 6   \n",
       "1                  4       2       0.0  ...  No Deposit                 9   \n",
       "2                  4       1       0.0  ...  No Deposit                 9   \n",
       "3                  4       2       0.0  ...  No Deposit                 9   \n",
       "4                  2       2       0.0  ...  No Deposit                 9   \n",
       "5                  3       2       0.0  ...  No Deposit                 9   \n",
       "6                  3       1       0.0  ...  No Deposit                 1   \n",
       "7                  3       1       0.0  ...  No Deposit                 9   \n",
       "8                  3       2       0.0  ...  No Deposit                 8   \n",
       "9                  3       2       0.0  ...  No Deposit                 8   \n",
       "\n",
       "       Company DaysInWaitingList     CustomerType    ADR  \\\n",
       "0         NULL                 0        Transient   0.00   \n",
       "1         NULL                 0        Transient  76.50   \n",
       "2         NULL                 0        Transient  68.00   \n",
       "3         NULL                 0        Transient  76.50   \n",
       "4         NULL                 0        Transient  76.50   \n",
       "5         NULL                 0        Transient  76.50   \n",
       "6         NULL                 0  Transient-Party  58.67   \n",
       "7         NULL                 0        Transient  68.00   \n",
       "8         NULL                 0        Transient  76.50   \n",
       "9         NULL                 0        Transient  76.50   \n",
       "\n",
       "   RequiredCarParkingSpaces  TotalOfSpecialRequests ReservationStatus  \\\n",
       "0                         0                       0         Check-Out   \n",
       "1                         0                       1          Canceled   \n",
       "2                         0                       1          Canceled   \n",
       "3                         0                       2          Canceled   \n",
       "4                         0                       1          Canceled   \n",
       "5                         0                       1          Canceled   \n",
       "6                         0                       0         Check-Out   \n",
       "7                         0                       0          Canceled   \n",
       "8                         0                       1           No-Show   \n",
       "9                         0                       1           No-Show   \n",
       "\n",
       "  ReservationStatusDate  \n",
       "0            2015-07-03  \n",
       "1            2015-07-01  \n",
       "2            2015-04-30  \n",
       "3            2015-06-23  \n",
       "4            2015-04-02  \n",
       "5            2015-06-25  \n",
       "6            2015-07-05  \n",
       "7            2015-06-25  \n",
       "8            2015-07-02  \n",
       "9            2015-07-02  \n",
       "\n",
       "[10 rows x 31 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "H2_dataset=pd.read_csv('H2.csv') # Reading the h2 csv\n",
    "H2_dataset.head(10) # Only showing first 10 rows of csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "UvJST6ka-DmH"
   },
   "source": [
    "Q: Do the two datasets have the same column names (features)?\n",
    "\n",
    "Ans: Yes they are same"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 50
    },
    "colab_type": "code",
    "id": "heI-QHSF-jbt",
    "outputId": "7875c672-e820-4630-d861-1eb626d873dc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "H1_dataset shape: (40060, 31)\n",
      "H2_dataset shape: (79330, 31)\n"
     ]
    }
   ],
   "source": [
    "print ('H1_dataset shape:',H1_dataset.shape) # for finding the rows and column in the dataset\n",
    "print ('H2_dataset shape:',H2_dataset.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "fh10WuD-8rnE",
    "outputId": "468e30a5-b31a-451d-ca4c-6d547a115ab9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "H_dataset shape: (119390, 31)\n"
     ]
    }
   ],
   "source": [
    "H_dataset = H1_dataset.append(H2_dataset, ignore_index=True) # H dataset made by combining the both datasets\n",
    "print ('H_dataset shape:',H_dataset.shape) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "c-ffBr9g_TtY"
   },
   "source": [
    "Q: How large the dataset is?\n",
    "\n",
    "Ans:  BELOW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 50
    },
    "colab_type": "code",
    "id": "AGegEpVC_IkM",
    "outputId": "546c72f2-2e61-4ed2-db55-f35663ebb918"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "H_dataset Number of rows: 119390\n",
      "H_dataset Number of Columns: 31\n"
     ]
    }
   ],
   "source": [
    "print ('H_dataset Number of rows:',H_dataset.shape[0]) \n",
    "print ('H_dataset Number of Columns:',H_dataset.shape[1]) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "aW3RNYIVi19x"
   },
   "source": [
    "### Exercise 2\n",
    "\n",
    "What are the names of the features of the dataset and what are their data types (i.e. are they numerical data? categorical? strings? If numerical, are they floats, ints, ...?)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 244
    },
    "colab_type": "code",
    "id": "FnTItdNT8rje",
    "outputId": "91f5f9bf-a130-4480-c01f-4b085a8ac7a2"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>IsCanceled</th>\n",
       "      <th>LeadTime</th>\n",
       "      <th>ArrivalDateYear</th>\n",
       "      <th>ArrivalDateMonth</th>\n",
       "      <th>ArrivalDateWeekNumber</th>\n",
       "      <th>ArrivalDateDayOfMonth</th>\n",
       "      <th>StaysInWeekendNights</th>\n",
       "      <th>StaysInWeekNights</th>\n",
       "      <th>Adults</th>\n",
       "      <th>Children</th>\n",
       "      <th>...</th>\n",
       "      <th>DepositType</th>\n",
       "      <th>Agent</th>\n",
       "      <th>Company</th>\n",
       "      <th>DaysInWaitingList</th>\n",
       "      <th>CustomerType</th>\n",
       "      <th>ADR</th>\n",
       "      <th>RequiredCarParkingSpaces</th>\n",
       "      <th>TotalOfSpecialRequests</th>\n",
       "      <th>ReservationStatus</th>\n",
       "      <th>ReservationStatusDate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>342</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>NULL</td>\n",
       "      <td>NULL</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>2015-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>737</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>NULL</td>\n",
       "      <td>NULL</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>2015-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>NULL</td>\n",
       "      <td>NULL</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>75.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>2015-07-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>304</td>\n",
       "      <td>NULL</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>75.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>2015-07-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>240</td>\n",
       "      <td>NULL</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>98.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>2015-07-03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   IsCanceled  LeadTime  ArrivalDateYear ArrivalDateMonth  \\\n",
       "0           0       342             2015             July   \n",
       "1           0       737             2015             July   \n",
       "2           0         7             2015             July   \n",
       "3           0        13             2015             July   \n",
       "4           0        14             2015             July   \n",
       "\n",
       "   ArrivalDateWeekNumber  ArrivalDateDayOfMonth  StaysInWeekendNights  \\\n",
       "0                     27                      1                     0   \n",
       "1                     27                      1                     0   \n",
       "2                     27                      1                     0   \n",
       "3                     27                      1                     0   \n",
       "4                     27                      1                     0   \n",
       "\n",
       "   StaysInWeekNights  Adults  Children  ...      DepositType        Agent  \\\n",
       "0                  0       2       0.0  ...  No Deposit              NULL   \n",
       "1                  0       2       0.0  ...  No Deposit              NULL   \n",
       "2                  1       1       0.0  ...  No Deposit              NULL   \n",
       "3                  1       1       0.0  ...  No Deposit               304   \n",
       "4                  2       2       0.0  ...  No Deposit               240   \n",
       "\n",
       "       Company DaysInWaitingList CustomerType   ADR  RequiredCarParkingSpaces  \\\n",
       "0         NULL                 0    Transient   0.0                         0   \n",
       "1         NULL                 0    Transient   0.0                         0   \n",
       "2         NULL                 0    Transient  75.0                         0   \n",
       "3         NULL                 0    Transient  75.0                         0   \n",
       "4         NULL                 0    Transient  98.0                         0   \n",
       "\n",
       "   TotalOfSpecialRequests ReservationStatus ReservationStatusDate  \n",
       "0                       0         Check-Out            2015-07-01  \n",
       "1                       0         Check-Out            2015-07-01  \n",
       "2                       0         Check-Out            2015-07-02  \n",
       "3                       0         Check-Out            2015-07-02  \n",
       "4                       1         Check-Out            2015-07-03  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "H_dataset.head(5) # showing first 5 entries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7X7PIUk9BQ4p"
   },
   "source": [
    "Q: What are the names of the features of the dataset and what are their data types?\n",
    "\n",
    "Ans: Shown Below\n",
    "\n",
    "\n",
    "---\n",
    "Column name--------------------- Data type\n",
    " ---\n",
    "---\n",
    "\n",
    "\n",
    "IsCanceled ---------------------------------------------------------- Categorical (int64)\n",
    "\n",
    "LeadTime ------------------------------------------------------------ numerical (int64)\n",
    "\n",
    "ArrivalDateYear --------------------------------------------------- numerical (int64)\n",
    "\n",
    "ArrivalDateMonth ----------------------------------------------- Categorical (object)\n",
    "\n",
    "ArrivalDateWeekNumber ------------------------------------ numerical (int64)\n",
    "\n",
    "ArrivalDateDayOfMonth ------------------------------------- numerical (int64)\n",
    "\n",
    "StaysInWeekendNights ------------------------------------- numerical (int64)\n",
    "\n",
    "StaysInWeekNights ------------------------------------------- numerical (int64)\n",
    "\n",
    "Adults ---------------------------------------------------------------- numerical (int64)\n",
    "\n",
    "Children ------------------------------------------------------------- numerical (float64)\n",
    "\n",
    "Babies --------------------------------------------------------------- numerical (int64)\n",
    "\n",
    "Meal ------------------------------------------------------------------ Categorical (object)\n",
    "\n",
    "Country ------------------------------------------------------------- Categorical (object)\n",
    "\n",
    "MarketSegment ------------------------------------------------ Categorical (object)\n",
    "\n",
    "DistributionChannel ------------------------------------------ Categorical (object)\n",
    "\n",
    "IsRepeatedGuest ---------------------------------------------- numerical (int64)\n",
    "\n",
    "PreviousCancellations ------------------------------------- numerical (int64)\n",
    "\n",
    "PreviousBookingsNotCanceled ----------------------- numerical (int64)\n",
    "\n",
    "ReservedRoomType ----------------------------------------- Categorical (object)\n",
    "\n",
    "AssignedRoomType ----------------------------------------- Categorical (object)\n",
    "\n",
    "BookingChanges ---------------------------------------------- numerical (int64)\n",
    "\n",
    "DepositType ----------------------------------------------------- Categorical (object)\n",
    "\n",
    "Agent --------------------------------------------------------------- Categorical (object)\n",
    "\n",
    "Company --------------------------------------------------------- Categorical (object)\n",
    "\n",
    "DaysInWaitingList ------------------------------------------- numerical (int64)\n",
    "\n",
    "CustomerType ------------------------------------------------ Categorical (object)\n",
    "\n",
    "ADR ----------------------------------------------------------------- numerical (float64)\n",
    "\n",
    "RequiredCarParkingSpaces ---------------------------- numerical (int64)\n",
    "\n",
    "TotalOfSpecialRequests --------------------------------- numerical (int64)\n",
    "\n",
    "ReservationStatus ------------------------------------------ Categorical (object)\n",
    "\n",
    "ReservationStatusDate ---------------------------------- Date (object)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 554
    },
    "colab_type": "code",
    "id": "evWSdpPL8rgE",
    "outputId": "b9525781-1789-4291-972e-3f015e54ce65"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "IsCanceled                       int64\n",
       "LeadTime                         int64\n",
       "ArrivalDateYear                  int64\n",
       "ArrivalDateMonth                object\n",
       "ArrivalDateWeekNumber            int64\n",
       "ArrivalDateDayOfMonth            int64\n",
       "StaysInWeekendNights             int64\n",
       "StaysInWeekNights                int64\n",
       "Adults                           int64\n",
       "Children                       float64\n",
       "Babies                           int64\n",
       "Meal                            object\n",
       "Country                         object\n",
       "MarketSegment                   object\n",
       "DistributionChannel             object\n",
       "IsRepeatedGuest                  int64\n",
       "PreviousCancellations            int64\n",
       "PreviousBookingsNotCanceled      int64\n",
       "ReservedRoomType                object\n",
       "AssignedRoomType                object\n",
       "BookingChanges                   int64\n",
       "DepositType                     object\n",
       "Agent                           object\n",
       "Company                         object\n",
       "DaysInWaitingList                int64\n",
       "CustomerType                    object\n",
       "ADR                            float64\n",
       "RequiredCarParkingSpaces         int64\n",
       "TotalOfSpecialRequests           int64\n",
       "ReservationStatus               object\n",
       "ReservationStatusDate           object\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "H_dataset.dtypes # For showing data types saving of pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "jUPeTgZli19y"
   },
   "source": [
    "### Exercise 3\n",
    "\n",
    "At this point, what other steps would you take to get a better understanding of the data? Does it need to be cleaned or manipulated in any way? Explain and execute."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "d9VjzXkLI3sv"
   },
   "source": [
    "#### Checking the missing values in each column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 571
    },
    "colab_type": "code",
    "id": "B58ZYleWAsUv",
    "outputId": "ed08d3ca-fa7e-4614-d82c-4a650ddc7eac"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing values per column:\n",
      "IsCanceled                       0\n",
      "LeadTime                         0\n",
      "ArrivalDateYear                  0\n",
      "ArrivalDateMonth                 0\n",
      "ArrivalDateWeekNumber            0\n",
      "ArrivalDateDayOfMonth            0\n",
      "StaysInWeekendNights             0\n",
      "StaysInWeekNights                0\n",
      "Adults                           0\n",
      "Children                         4\n",
      "Babies                           0\n",
      "Meal                             0\n",
      "Country                        488\n",
      "MarketSegment                    0\n",
      "DistributionChannel              0\n",
      "IsRepeatedGuest                  0\n",
      "PreviousCancellations            0\n",
      "PreviousBookingsNotCanceled      0\n",
      "ReservedRoomType                 0\n",
      "AssignedRoomType                 0\n",
      "BookingChanges                   0\n",
      "DepositType                      0\n",
      "Agent                            0\n",
      "Company                          0\n",
      "DaysInWaitingList                0\n",
      "CustomerType                     0\n",
      "ADR                              0\n",
      "RequiredCarParkingSpaces         0\n",
      "TotalOfSpecialRequests           0\n",
      "ReservationStatus                0\n",
      "ReservationStatusDate            0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#Applying per column:\n",
    "print (\"Missing values per column:\")\n",
    "print (H_dataset.isnull().sum(axis = 0) )#axis=0 defines that function is to be applied on each column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "o1xtJfjqvHKV"
   },
   "source": [
    "Removing the missing values rows so that they dont effect on our model. Below we have checked again so no repeatable value found."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Pw-T_mj0AsX-"
   },
   "outputs": [],
   "source": [
    "H_dataset=H_dataset.dropna() # there are number of values those are nan, null or Na in country and children thats why we need to remove them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 571
    },
    "colab_type": "code",
    "id": "wJWDisMbkygt",
    "outputId": "17f5bfdf-da66-4cdf-8d83-7020f73627e0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing values per column:\n",
      "IsCanceled                     0\n",
      "LeadTime                       0\n",
      "ArrivalDateYear                0\n",
      "ArrivalDateMonth               0\n",
      "ArrivalDateWeekNumber          0\n",
      "ArrivalDateDayOfMonth          0\n",
      "StaysInWeekendNights           0\n",
      "StaysInWeekNights              0\n",
      "Adults                         0\n",
      "Children                       0\n",
      "Babies                         0\n",
      "Meal                           0\n",
      "Country                        0\n",
      "MarketSegment                  0\n",
      "DistributionChannel            0\n",
      "IsRepeatedGuest                0\n",
      "PreviousCancellations          0\n",
      "PreviousBookingsNotCanceled    0\n",
      "ReservedRoomType               0\n",
      "AssignedRoomType               0\n",
      "BookingChanges                 0\n",
      "DepositType                    0\n",
      "Agent                          0\n",
      "Company                        0\n",
      "DaysInWaitingList              0\n",
      "CustomerType                   0\n",
      "ADR                            0\n",
      "RequiredCarParkingSpaces       0\n",
      "TotalOfSpecialRequests         0\n",
      "ReservationStatus              0\n",
      "ReservationStatusDate          0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#After removing missing values let check them also\n",
    "print (\"Missing values per column:\")\n",
    "print (H_dataset.isnull().sum(axis = 0) )#axis=0 defines that function is to be applied on each column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "io4Jt3C5AsR9"
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "BwMKdGxfvHKr"
   },
   "source": [
    "#### Checking the distribution of some features on histrogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 314
    },
    "colab_type": "code",
    "id": "RQIjDTl_AsPn",
    "outputId": "cc43d060-b036-4d0d-9f3f-e374f8abbb5f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f6e5fb832b0>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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McgvwVWABuLyqfgKQ5G3AncAxwHVV9eBhGK8kqdMhCY2qmgfm2/QjDJ58Wtznb4BfPcD6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\ngg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "H_dataset.hist(column=\"Children\",bins=[0,1,2,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 314
    },
    "colab_type": "code",
    "id": "CwStpnvLAsM9",
    "outputId": "fb595bea-409a-48f3-c779-654ab7d5bdd1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f6e5fb5a208>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gI4FV5TrE84CbMvPmiLgH6I+IS4HvAteW/tcCfxcRg8BOmg98MnNLRNwE3AM8A1xUTl0R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\nhCSpyoCQJFUZEJKkKgNCklRlQEiSqv4/oRFhoFeN7uEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "H_dataset.hist(column=\"ArrivalDateYear\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "EaR1pgJD8rdi"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "-mHfM5lEi19z"
   },
   "source": [
    "## Part 2: Diving Into Some Details\n",
    "\n",
    "Now that we have cleaned the data, put it in a friendly format, and have a basic understanding of it, lets dive deeper into some details."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "WnjdJ4jei190"
   },
   "source": [
    "### Exercise 1\n",
    "\n",
    "What is the distribution of the average daily rate (ADR)? Do you notice anything peculiar about this distribution? If so, and if you think something should be done about it, state it and do that. Also, approximate the mean and standard deviation of this distribution by eye, and then compare with the actual values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "sSlUflcMSpCJ"
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 286
    },
    "colab_type": "code",
    "id": "20J8GAfcSjww",
    "outputId": "867c59fa-440c-446c-d9e3-7e69eb8202fd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f6e5d1eaa90>]"
      ]
     },
     "execution_count": 17,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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k/z/bYGYfmlmboGlhveaV6deQnHP16kHgq7F3Ar2BxsDXQN9ot8u3rTNwlR8+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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(H_dataset['ADR'].values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "uIvE1RhtYYND"
   },
   "source": [
    "There is one value that is way to big then other values.\n",
    "\n",
    "We need to remove it as it as acting like an error pulse which will effect on model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "hv0iQ7YbZbyy"
   },
   "outputs": [],
   "source": [
    "H_dataset.drop(H_dataset['ADR'].idxmax(), inplace=True) # Removing the row by finding the biggest value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 286
    },
    "colab_type": "code",
    "id": "uQ4fQj4AZ0Sw",
    "outputId": "67921057-aadc-4803-a693-eace33414ac9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f6e5d1c7da0>]"
      ]
     },
     "execution_count": 19,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cGzguEsHG/XXsqWlKuGyq7LRKnXHzXEfXmQ/WWrX8+ubg961X2RXoE7l+TOh2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\nEU36SimVR/4/BP20fTWOWcAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(H_dataset['ADR'].values) # after Removing the spike value the data distrubution shown below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "k8gHdt87YXMs"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "lo1tYt_avHLb"
   },
   "source": [
    "#### Standardize the ADR feature\n",
    "Standardize features by removing the mean and scaling to unit variance\n",
    "\n",
    "The standard score of a sample x is calculated as:\n",
    "\n",
    "z = (x - u) / s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Ejqi1BXfUi5f"
   },
   "source": [
    "When our data is comprised of attributes with varying scales, many machine learning algorithms can benefit from rescaling the attributes to all have the same scale.\n",
    " \n",
    "Standardization is a useful technique to transform attributes with a Gaussian distribution and differing means and standard deviations to a standard Gaussian distribution with a mean of 0 and a standard deviation of 1.\n",
    " \n",
    "• We can standardize data using scikit-learn with the StandardScaler class\n",
    " \n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "YbiL-Qd2WHvj"
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "NuUkAr5aYAG0"
   },
   "outputs": [],
   "source": [
    "cols_to_norm = ['ADR']\n",
    "H_dataset[cols_to_norm] = StandardScaler().fit_transform(H_dataset[cols_to_norm])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 286
    },
    "colab_type": "code",
    "id": "uvaHuTJ-WHmI",
    "outputId": "f9a69084-6b2a-4efe-d162-a2ac83b50f7d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f6e532a5518>]"
      ]
     },
     "execution_count": 22,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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avySGLhLs+Jzsyr1+53ub8fOzj7C8nXhBsyOg57OwGpQ/M5HaotKGK7BElFIZvdIedTV5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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(H_dataset['ADR'].values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "aOw2n-G1d145"
   },
   "source": [
    "By checking with just eye it looks that mean is near to zero and std is near to 1, we can verify it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "WUIeSfQaWfDl",
    "outputId": "575b5522-cf41-4bc8-b99e-c46400b13fa8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ADR Mean:  -2.6390545775895125e-16\n"
     ]
    }
   ],
   "source": [
    "ADR_Mean=H_dataset['ADR'].values.mean()\n",
    "print ('ADR Mean: ',ADR_Mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "A2zGAGKEYADH",
    "outputId": "56d0427c-23b6-422d-8c5f-efc0f966a5d7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ADR Standard deviation:  1.0\n"
     ]
    }
   ],
   "source": [
    "ADR_Std=H_dataset['ADR'].values.std()\n",
    "print ('ADR Standard deviation: ',ADR_Std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "W0ei-kF8X_7x"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "NeuDKn8qi191"
   },
   "source": [
    "### Exercise 2\n",
    "\n",
    "What is the distribution of values of the total length of stay? Do you notice anything noteworthy about it?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "VIr3oRDSvHMM"
   },
   "source": [
    "Below some graph have been shown to check the variation and distribution in stay features(StaysInWeekendNights,StaysInWeekNights). What I have notice that these two features follows some similiar trends."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 336
    },
    "colab_type": "code",
    "id": "3MIBphBtX_4N",
    "outputId": "3d578fe8-e4fd-47a0-a59b-19b8486fdc21"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([8.2205e+04, 3.4502e+04, 1.8490e+03, 2.3100e+02, 1.9000e+01,\n",
       "        7.1000e+01, 7.0000e+00, 5.0000e+00, 5.0000e+00, 3.0000e+00]),\n",
       " array([ 0. ,  1.6,  3.2,  4.8,  6.4,  8. ,  9.6, 11.2, 12.8, 14.4, 16. ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 25,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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qAeAt09QfZXD9YWr9x8B7D7Otq4Grp6lvB7aP0K8k6QTwHdKSpA7DQZLUYThIkjoMB0lS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\nw0GS1GE4SJI6DAdJUofhIEnq+P8mCUoCz3qDTAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(H_dataset['StaysInWeekendNights'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 336
    },
    "colab_type": "code",
    "id": "-QBJQhfoX_zZ",
    "outputId": "67358f21-33e7-4c57-d854-8b9793a39745"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([1.03014e+05, 1.42230e+04, 1.35800e+03, 1.63000e+02, 9.50000e+01,\n",
       "        2.50000e+01, 7.00000e+00, 6.00000e+00, 3.00000e+00, 3.00000e+00]),\n",
       " array([ 0. ,  4.1,  8.2, 12.3, 16.4, 20.5, 24.6, 28.7, 32.8, 36.9, 41. ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 26,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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itodGlyTrgHVt8RdJHp3hrk4HfnpsRvWadlzPU258VQ93XM/Vq8h56vNKztMf93Sa7aGx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\nrkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(H_dataset['StaysInWeekNights'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 286
    },
    "colab_type": "code",
    "id": "1ixx24l5e04u",
    "outputId": "c6a116cb-fb76-4ede-b319-418f74e02a0d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f6e5346e9b0>]"
      ]
     },
     "execution_count": 27,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zac65FT7lTUIufhdUfWy5p3mHaoxibcmlrsvUF2Snm16scu8O5lcnv5k9D9zv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\niWGyjVwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(H_dataset['StaysInWeekendNights'].values)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 286
    },
    "colab_type": "code",
    "id": "xa7dqr18e08f",
    "outputId": "47f77f4c-9c0a-4ba8-a261-8e4089bc3ea5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f6e533d3a58>]"
      ]
     },
     "execution_count": 28,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TEfnB75ZjDO5ERD5gzp2IiBxjcCciiiAGd6IQYs/e6PG7My+DOxGRD/zuy8vg\nThRC3x1+atBJIJedcuIgX9fH4E4UQv915OlBJ4FcdsoQBnei2GOJOznF4E4UQqxPJacY3IlC6JWl\nejyujsKLwZ0ohGoOtOWfiSgHBncioghicCcKoSGD+dMkZ3gGEYXQSYP40yRneAYRhRBz7uQUzyCi\nEPrZ3/910EkgzTkK7iIyTkQqRKRKRCa6lSiiuPvttRf0va55egIqnxgfYGpIR7aDu4gMAjAVwHgA\nowHcJiKj3UoYERHZ5yTnfimAKqVUtVKqE8BsADe5kywiInLCSXD/DoA9Ke9rjWlE5DK/xwIn/Xle\noSoid4pIsYgUNzY22lrGv4wZ4XKqyAujh/8lAGD8987CxPGj8NhNFx43z49HfRtD8jTzu2jEt3Dd\n98/CqScN7pt27egzceNFZ+OMb54EIPE8yuHfOhlXjfp23zxmnlF58TlDE+s4ZyhuuOhsXPf9s3Db\npX+Ne358PkadNXCY3RMHZV/g2AvPxF8MGYTx3zsL4y48CwBw26Xn4MEJ38Udl/8NAGDi+FG47dLM\nFaNXXjAs67J/8d9GAgAm/Ofh+OUViddDBp+Ay87jSJG6+uKe/+H7OkXZzBKIyOUAHlFKjTXe3wcA\nSqmnsn1nzJgxqri42Nb6iIjiSkTWK6XGWPmOk5z7OgDni8i5IjIEwK0APnewPCIicsng/LNkppTq\nFpHfAFgEYBCAGUqpUtdSRkREttkO7gCglFoAYIFLaSEiIpewhyoRUQQxuBMRRRCDOxFRBDG4ExFF\nEIM7EVEE2e7EZGtlIo0Adtn8+hkAmlxMTpC4LeEVpe3htoSTnW35G6VU9m7NGfga3J0QkWKrPbTC\nitsSXlHaHm5LOPm1LSyWISKKIAZ3IqII0im4Tw86AS7itoRXlLaH2xJOvmyLNmXuRERknk45dyIi\nMkmL4B7GB3GLyDkiUigi20SkVETuMaafLiKLRaTS+H+aMV1E5CVjG0pE5Acpy7rDmL9SRO5Imf5f\nRGSL8Z2XRMTE4ygcbdMgEdkoIvOM9+eKSJGx/g+MoZ0hIicZ76uMz0emLOM+Y3qFiIxNme7rMRSR\noSLykYiUi0iZiFyu67ERkf9tnGNbReR9ETlZl2MjIjNEpEFEtqZM8/w4ZFuHB9vynHGOlYjIpyIy\nNOUzS/vbzjHNSSkV6j8khhPeAeA8AEMAbAYwOgTpGg7gB8brUwFsR+JB4c8CmGhMnwjgGeP1dQC+\nACAALgNQZEw/HUC18f804/VpxmdrjXnF+O54j7fptwBmAZhnvP8QwK3G61cB/Jvx+i4ArxqvbwXw\ngfF6tHF8TgJwrnHcBgVxDAGzu103AAADqklEQVTMBPCvxushAIbqeGyQeHTlTgCnpByTX+hybAD8\nEMAPAGxNmeb5cci2Dg+25VoAg43Xz6Rsi+X9bfWY5k2vlz8wl06OywEsSnl/H4D7gk5XhnTOAXAN\ngAoAw41pwwFUGK+nAbgtZf4K4/PbAExLmT7NmDYcQHnK9AHzeZD+EQAKAFwFYJ7xY2lKOXH7jgMS\nY/hfbrwebMwn6ccmOZ/fxxDAt5AIiJI2Xbtjg/5nFZ9u7Ot5AMbqdGwAjMTAgOj5cci2Dre3Je2z\nfwDwXqb9mG9/2/m95UurDsUyoX8Qt3GbdAmAIgBnKqXqjI/2AzjTeJ1tO3JNr80w3SuTAfwOQK/x\n/q8AHFJKdWdYf1+ajc9bjPmtbqNXzgXQCOBNSRQzvS4i34CGx0YptRfAHwHsBlCHxL5eD32PDeDP\ncci2Di/9Com7B8D6ttj5veWkQ3APNRH5JoCPAdyrlDqc+plKXGpD3xxJRK4H0KCUWh90WlwyGInb\n51eUUpcAOIrErXkfjY7NaQBuQuKCdTaAbwAYF2iiXOTHcfBjHSLyAIBuAO95uR4rdAjuewGck/J+\nhDEtcCJyIhKB/T2l1CfG5HoRGW58PhxAgzE923bkmj4iw3QvXAHgRhGpATAbiaKZFwEMFZHk07pS\n19+XZuPzbwE4AOvb6JVaALVKqSLj/UdIBHsdj83VAHYqpRqVUl0APkHieOl6bAB/jkO2dbhORH4B\n4HoAPzMuJMiT5kzTD8D6Mc3Ni3JCl8vrBiNRgXIu+isgLgxBugTA2wAmp01/DgMrcp41Xk/AwMqi\ntcb005EoHz7N+NsJ4HTjs/TKout82K4r0V+h+mcMrOC5y3h9NwZW8HxovL4QAyuRqpGoQPL9GAJY\nDuAC4/UjxnHR7tgA+HsApQD+wljXTAD/rtOxwfFl7p4fh2zr8GBbxgHYBmBY2nyW97fVY5o3rV7+\nwFw8Oa5DojXKDgAPBJ0eI03/HYlbvRIAm4y/65AoCysAUAngq5STUABMNbZhC4AxKcv6FYAq4++X\nKdPHANhqfGcKTFSiuLBdV6I/uJ9n/HiqjBPvJGP6ycb7KuPz81K+/4CR3gqktCDx+xgCuBhAsXF8\nPjOCgpbHBsCjAMqN9b1jBAwtjg2A95GoK+hC4o7q134ch2zr8GBbqpAoD0/GgFft7m87xzTXH3uo\nEhFFkA5l7kREZBGDOxFRBDG4ExFFEIM7EVEEMbgTEUUQgzsRUQQxuBMRRRCDOxFRBP1/xjKjNpmG\njJUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(H_dataset['StaysInWeekNights'].values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "AavGIubQi192"
   },
   "source": [
    "### Exercise 3\n",
    "\n",
    "What is the distribution of countries which people booking stays are coming from? Report this answer as percentages. What does this tell you about the probable location of the hotels?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "kuo1I9PTgA0Q"
   },
   "outputs": [],
   "source": [
    "lst=H_dataset['Country'] # Making the list of coutries\n",
    "cleanedList = [x for x in lst if str(x) != 'nan'] # Removing the values that are not available or NULL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "aMxphvRPgA5_"
   },
   "outputs": [],
   "source": [
    "countries_count=np.array(np.unique(cleanedList, return_counts=True)).T # Counting unique countries in the dataset\n",
    "\n",
    "countries_count=countries_count.astype('object')\n",
    "\n",
    "\n",
    "percentages=(np.true_divide(countries_count[:,1].astype('int64'), np.sum(countries_count[:,1].astype('int'))))*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1882
    },
    "colab_type": "code",
    "id": "x-DgXqs3gA97",
    "outputId": "40a34ec5-9ac2-4594-fcba-ea7a211cf514"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Count</th>\n",
       "      <th>Percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ABW</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00168213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AGO</td>\n",
       "      <td>362</td>\n",
       "      <td>0.304465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AIA</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000841064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ALB</td>\n",
       "      <td>12</td>\n",
       "      <td>0.0100928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AND</td>\n",
       "      <td>7</td>\n",
       "      <td>0.00588745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ARE</td>\n",
       "      <td>51</td>\n",
       "      <td>0.0428943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ARG</td>\n",
       "      <td>214</td>\n",
       "      <td>0.179988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ARM</td>\n",
       "      <td>8</td>\n",
       "      <td>0.00672851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ASM</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000841064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ATA</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00168213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>ATF</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000841064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>AUS</td>\n",
       "      <td>426</td>\n",
       "      <td>0.358293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>AUT</td>\n",
       "      <td>1263</td>\n",
       "      <td>1.06226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>AZE</td>\n",
       "      <td>17</td>\n",
       "      <td>0.0142981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>BDI</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000841064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>BEL</td>\n",
       "      <td>2342</td>\n",
       "      <td>1.96977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>BEN</td>\n",
       "      <td>3</td>\n",
       "      <td>0.00252319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>BFA</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000841064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>BGD</td>\n",
       "      <td>12</td>\n",
       "      <td>0.0100928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>BGR</td>\n",
       "      <td>75</td>\n",
       "      <td>0.0630798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>BHR</td>\n",
       "      <td>5</td>\n",
       "      <td>0.00420532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>BHS</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000841064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>BIH</td>\n",
       "      <td>13</td>\n",
       "      <td>0.0109338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>BLR</td>\n",
       "      <td>26</td>\n",
       "      <td>0.0218677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>BOL</td>\n",
       "      <td>10</td>\n",
       "      <td>0.00841064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>BRA</td>\n",
       "      <td>2224</td>\n",
       "      <td>1.87053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>BRB</td>\n",
       "      <td>4</td>\n",
       "      <td>0.00336426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>BWA</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000841064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CAF</td>\n",
       "      <td>5</td>\n",
       "      <td>0.00420532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>CHE</td>\n",
       "      <td>1730</td>\n",
       "      <td>1.45504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>SLV</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00168213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>SMR</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000841064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>SRB</td>\n",
       "      <td>101</td>\n",
       "      <td>0.0849475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>STP</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00168213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>SUR</td>\n",
       "      <td>5</td>\n",
       "      <td>0.00420532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>SVK</td>\n",
       "      <td>65</td>\n",
       "      <td>0.0546692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>SVN</td>\n",
       "      <td>57</td>\n",
       "      <td>0.0479407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>SWE</td>\n",
       "      <td>1024</td>\n",
       "      <td>0.86125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>SYC</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00168213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>SYR</td>\n",
       "      <td>3</td>\n",
       "      <td>0.00252319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>TGO</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00168213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>THA</td>\n",
       "      <td>59</td>\n",
       "      <td>0.0496228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>TJK</td>\n",
       "      <td>9</td>\n",
       "      <td>0.00756958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>TMP</td>\n",
       "      <td>3</td>\n",
       "      <td>0.00252319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>TUN</td>\n",
       "      <td>39</td>\n",
       "      <td>0.0328015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>TUR</td>\n",
       "      <td>248</td>\n",
       "      <td>0.208584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>TWN</td>\n",
       "      <td>51</td>\n",
       "      <td>0.0428943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>TZA</td>\n",
       "      <td>5</td>\n",
       "      <td>0.00420532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>UGA</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00168213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>UKR</td>\n",
       "      <td>68</td>\n",
       "      <td>0.0571924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>UMI</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000841064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>URY</td>\n",
       "      <td>32</td>\n",
       "      <td>0.0269141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>USA</td>\n",
       "      <td>2097</td>\n",
       "      <td>1.76371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>UZB</td>\n",
       "      <td>4</td>\n",
       "      <td>0.00336426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>VEN</td>\n",
       "      <td>26</td>\n",
       "      <td>0.0218677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>VGB</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000841064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>VNM</td>\n",
       "      <td>8</td>\n",
       "      <td>0.00672851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>ZAF</td>\n",
       "      <td>80</td>\n",
       "      <td>0.0672851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>ZMB</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00168213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>4</td>\n",
       "      <td>0.00336426</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>177 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Country Count   Percentage\n",
       "0       ABW     2   0.00168213\n",
       "1       AGO   362     0.304465\n",
       "2       AIA     1  0.000841064\n",
       "3       ALB    12    0.0100928\n",
       "4       AND     7   0.00588745\n",
       "5       ARE    51    0.0428943\n",
       "6       ARG   214     0.179988\n",
       "7       ARM     8   0.00672851\n",
       "8       ASM     1  0.000841064\n",
       "9       ATA     2   0.00168213\n",
       "10      ATF     1  0.000841064\n",
       "11      AUS   426     0.358293\n",
       "12      AUT  1263      1.06226\n",
       "13      AZE    17    0.0142981\n",
       "14      BDI     1  0.000841064\n",
       "15      BEL  2342      1.96977\n",
       "16      BEN     3   0.00252319\n",
       "17      BFA     1  0.000841064\n",
       "18      BGD    12    0.0100928\n",
       "19      BGR    75    0.0630798\n",
       "20      BHR     5   0.00420532\n",
       "21      BHS     1  0.000841064\n",
       "22      BIH    13    0.0109338\n",
       "23      BLR    26    0.0218677\n",
       "24      BOL    10   0.00841064\n",
       "25      BRA  2224      1.87053\n",
       "26      BRB     4   0.00336426\n",
       "27      BWA     1  0.000841064\n",
       "28      CAF     5   0.00420532\n",
       "29      CHE  1730      1.45504\n",
       "..      ...   ...          ...\n",
       "147     SLV     2   0.00168213\n",
       "148     SMR     1  0.000841064\n",
       "149     SRB   101    0.0849475\n",
       "150     STP     2   0.00168213\n",
       "151     SUR     5   0.00420532\n",
       "152     SVK    65    0.0546692\n",
       "153     SVN    57    0.0479407\n",
       "154     SWE  1024      0.86125\n",
       "155     SYC     2   0.00168213\n",
       "156     SYR     3   0.00252319\n",
       "157     TGO     2   0.00168213\n",
       "158     THA    59    0.0496228\n",
       "159     TJK     9   0.00756958\n",
       "160     TMP     3   0.00252319\n",
       "161     TUN    39    0.0328015\n",
       "162     TUR   248     0.208584\n",
       "163     TWN    51    0.0428943\n",
       "164     TZA     5   0.00420532\n",
       "165     UGA     2   0.00168213\n",
       "166     UKR    68    0.0571924\n",
       "167     UMI     1  0.000841064\n",
       "168     URY    32    0.0269141\n",
       "169     USA  2097      1.76371\n",
       "170     UZB     4   0.00336426\n",
       "171     VEN    26    0.0218677\n",
       "172     VGB     1  0.000841064\n",
       "173     VNM     8   0.00672851\n",
       "174     ZAF    80    0.0672851\n",
       "175     ZMB     2   0.00168213\n",
       "176     ZWE     4   0.00336426\n",
       "\n",
       "[177 rows x 3 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Results=np.c_[countries_count,percentages]\n",
    "pd.DataFrame(Results,columns=['Country','Count','Percentage'])  # it show country name, count and percentage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "9NRF-ijLofWX",
    "outputId": "d800797d-889f-4546-fd7b-cb50d1b6e582"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['PRT', '48585', 40.86309999411255], dtype=object)"
      ]
     },
     "execution_count": 32,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Results[np.argmax(percentages)] # maximum number of stay in PRT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "gfEnzI2sg2mS"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ZooflhkIi194"
   },
   "source": [
    "### Exercise 4\n",
    "\n",
    "How would you investigate the relationships between the various features of the data? Explain and demonstrate."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "S1gFGomPTGiq"
   },
   "source": [
    "By finding the correlation between different data we can find relationship between different features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Hu1VLlsaofJG"
   },
   "outputs": [],
   "source": [
    "corr_matrix = H_dataset.corr() # calculating correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 600
    },
    "colab_type": "code",
    "id": "veHh3IAnrmK8",
    "outputId": "e80228ed-7665-432a-aa20-fb4f477ce9c8"
   },
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>IsCanceled</th>\n",
       "      <th>LeadTime</th>\n",
       "      <th>ArrivalDateYear</th>\n",
       "      <th>ArrivalDateWeekNumber</th>\n",
       "      <th>ArrivalDateDayOfMonth</th>\n",
       "      <th>StaysInWeekendNights</th>\n",
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       "      <th>IsCanceled</th>\n",
       "      <td>1.000000</td>\n",
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       "      <td>0.058379</td>\n",
       "      <td>0.004754</td>\n",
       "      <td>-0.032520</td>\n",
       "      <td>-0.085178</td>\n",
       "      <td>0.109924</td>\n",
       "      <td>-0.055493</td>\n",
       "      <td>-0.144674</td>\n",
       "      <td>0.054018</td>\n",
       "      <td>0.047294</td>\n",
       "      <td>-0.194795</td>\n",
       "      <td>-0.235638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LeadTime</th>\n",
       "      <td>0.292004</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.039923</td>\n",
       "      <td>0.126733</td>\n",
       "      <td>0.002325</td>\n",
       "      <td>0.083980</td>\n",
       "      <td>0.164780</td>\n",
       "      <td>0.116801</td>\n",
       "      <td>-0.038336</td>\n",
       "      <td>-0.021150</td>\n",
       "      <td>-0.125093</td>\n",
       "      <td>0.085960</td>\n",
       "      <td>-0.071128</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.170006</td>\n",
       "      <td>-0.069086</td>\n",
       "      <td>-0.115562</td>\n",
       "      <td>-0.096540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ArrivalDateYear</th>\n",
       "      <td>0.016415</td>\n",
       "      <td>0.039923</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.540498</td>\n",
       "      <td>-0.000588</td>\n",
       "      <td>0.021668</td>\n",
       "      <td>0.031753</td>\n",
       "      <td>0.029147</td>\n",
       "      <td>0.054492</td>\n",
       "      <td>-0.013183</td>\n",
       "      <td>0.010128</td>\n",
       "      <td>-0.119922</td>\n",
       "      <td>0.029800</td>\n",
       "      <td>0.031126</td>\n",
       "      <td>-0.056823</td>\n",
       "      <td>0.207916</td>\n",
       "      <td>-0.012661</td>\n",
       "      <td>0.108926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ArrivalDateWeekNumber</th>\n",
       "      <td>0.007477</td>\n",
       "      <td>0.126733</td>\n",
       "      <td>-0.540498</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.066849</td>\n",
       "      <td>0.017634</td>\n",
       "      <td>0.015000</td>\n",
       "      <td>0.025397</td>\n",
       "      <td>0.005491</td>\n",
       "      <td>0.010038</td>\n",
       "      <td>-0.030413</td>\n",
       "      <td>0.035366</td>\n",
       "      <td>-0.020769</td>\n",
       "      <td>0.005197</td>\n",
       "      <td>0.022992</td>\n",
       "      <td>0.079992</td>\n",
       "      <td>0.001715</td>\n",
       "      <td>0.025772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ArrivalDateDayOfMonth</th>\n",
       "      <td>-0.006138</td>\n",
       "      <td>0.002325</td>\n",
       "      <td>-0.000588</td>\n",
       "      <td>0.066849</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.015911</td>\n",
       "      <td>-0.027601</td>\n",
       "      <td>-0.001839</td>\n",
       "      <td>0.014564</td>\n",
       "      <td>-0.000539</td>\n",
       "      <td>-0.006339</td>\n",
       "      <td>-0.027012</td>\n",
       "      <td>0.000118</td>\n",
       "      <td>0.010757</td>\n",
       "      <td>0.022738</td>\n",
       "      <td>0.030443</td>\n",
       "      <td>0.008265</td>\n",
       "      <td>0.003089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StaysInWeekendNights</th>\n",
       "      <td>-0.002621</td>\n",
       "      <td>0.083980</td>\n",
       "      <td>0.021668</td>\n",
       "      <td>0.017634</td>\n",
       "      <td>-0.015911</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.494885</td>\n",
       "      <td>0.090412</td>\n",
       "      <td>0.045428</td>\n",
       "      <td>0.018396</td>\n",
       "      <td>-0.087837</td>\n",
       "      <td>-0.013009</td>\n",
       "      <td>-0.040598</td>\n",
       "      <td>0.062411</td>\n",
       "      <td>-0.054569</td>\n",
       "      <td>0.050519</td>\n",
       "      <td>-0.018149</td>\n",
       "      <td>0.071664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StaysInWeekNights</th>\n",
       "      <td>0.024119</td>\n",
       "      <td>0.164780</td>\n",
       "      <td>0.031753</td>\n",
       "      <td>0.015000</td>\n",
       "      <td>-0.027601</td>\n",
       "      <td>0.494885</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.092001</td>\n",
       "      <td>0.044258</td>\n",
       "      <td>0.020157</td>\n",
       "      <td>-0.097996</td>\n",
       "      <td>-0.014274</td>\n",
       "      <td>-0.047368</td>\n",
       "      <td>0.095673</td>\n",
       "      <td>-0.002162</td>\n",
       "      <td>0.067530</td>\n",
       "      <td>-0.024379</td>\n",
       "      <td>0.066781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adults</th>\n",
       "      <td>0.058379</td>\n",
       "      <td>0.116801</td>\n",
       "      <td>0.029147</td>\n",
       "      <td>0.025397</td>\n",
       "      <td>-0.001839</td>\n",
       "      <td>0.090412</td>\n",
       "      <td>0.092001</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.029591</td>\n",
       "      <td>0.017887</td>\n",
       "      <td>-0.147494</td>\n",
       "      <td>-0.006974</td>\n",
       "      <td>-0.105028</td>\n",
       "      <td>-0.052423</td>\n",
       "      <td>-0.008765</td>\n",
       "      <td>0.238581</td>\n",
       "      <td>0.016370</td>\n",
       "      <td>0.121816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Children</th>\n",
       "      <td>0.004754</td>\n",
       "      <td>-0.038336</td>\n",
       "      <td>0.054492</td>\n",
       "      <td>0.005491</td>\n",
       "      <td>0.014564</td>\n",
       "      <td>0.045428</td>\n",
       "      <td>0.044258</td>\n",
       "      <td>0.029591</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.024130</td>\n",
       "      <td>-0.032990</td>\n",
       "      <td>-0.024753</td>\n",
       "      <td>-0.020365</td>\n",
       "      <td>0.048663</td>\n",
       "      <td>-0.033396</td>\n",
       "      <td>0.341462</td>\n",
       "      <td>0.057060</td>\n",
       "      <td>0.081784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Babies</th>\n",
       "      <td>-0.032520</td>\n",
       "      <td>-0.021150</td>\n",
       "      <td>-0.013183</td>\n",
       "      <td>0.010038</td>\n",
       "      <td>-0.000539</td>\n",
       "      <td>0.018396</td>\n",
       "      <td>0.020157</td>\n",
       "      <td>0.017887</td>\n",
       "      <td>0.024130</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.008955</td>\n",
       "      <td>-0.007490</td>\n",
       "      <td>-0.006306</td>\n",
       "      <td>0.083221</td>\n",
       "      <td>-0.010648</td>\n",
       "      <td>0.030090</td>\n",
       "      <td>0.036970</td>\n",
       "      <td>0.097600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IsRepeatedGuest</th>\n",
       "      <td>-0.085178</td>\n",
       "      <td>-0.125093</td>\n",
       "      <td>0.010128</td>\n",
       "      <td>-0.030413</td>\n",
       "      <td>-0.006339</td>\n",
       "      <td>-0.087837</td>\n",
       "      <td>-0.097996</td>\n",
       "      <td>-0.147494</td>\n",
       "      <td>-0.032990</td>\n",
       "      <td>-0.008955</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.082376</td>\n",
       "      <td>0.423259</td>\n",
       "      <td>0.012166</td>\n",
       "      <td>-0.022323</td>\n",
       "      <td>-0.141962</td>\n",
       "      <td>0.077776</td>\n",
       "      <td>0.013150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PreviousCancellations</th>\n",
       "      <td>0.109924</td>\n",
       "      <td>0.085960</td>\n",
       "      <td>-0.119922</td>\n",
       "      <td>0.035366</td>\n",
       "      <td>-0.027012</td>\n",
       "      <td>-0.013009</td>\n",
       "      <td>-0.014274</td>\n",
       "      <td>-0.006974</td>\n",
       "      <td>-0.024753</td>\n",
       "      <td>-0.007490</td>\n",
       "      <td>0.082376</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.154285</td>\n",
       "      <td>-0.027092</td>\n",
       "      <td>0.005927</td>\n",
       "      <td>-0.069117</td>\n",
       "      <td>-0.018455</td>\n",
       "      <td>-0.048586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PreviousBookingsNotCanceled</th>\n",
       "      <td>-0.055493</td>\n",
       "      <td>-0.071128</td>\n",
       "      <td>0.029800</td>\n",
       "      <td>-0.020769</td>\n",
       "      <td>0.000118</td>\n",
       "      <td>-0.040598</td>\n",
       "      <td>-0.047368</td>\n",
       "      <td>-0.105028</td>\n",
       "      <td>-0.020365</td>\n",
       "      <td>-0.006306</td>\n",
       "      <td>0.423259</td>\n",
       "      <td>0.154285</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.011971</td>\n",
       "      <td>-0.009011</td>\n",
       "      <td>-0.073024</td>\n",
       "      <td>0.046945</td>\n",
       "      <td>0.037595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BookingChanges</th>\n",
       "      <td>-0.144674</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.031126</td>\n",
       "      <td>0.005197</td>\n",
       "      <td>0.010757</td>\n",
       "      <td>0.062411</td>\n",
       "      <td>0.095673</td>\n",
       "      <td>-0.052423</td>\n",
       "      <td>0.048663</td>\n",
       "      <td>0.083221</td>\n",
       "      <td>0.012166</td>\n",
       "      <td>-0.027092</td>\n",
       "      <td>0.011971</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.011660</td>\n",
       "      <td>0.019052</td>\n",
       "      <td>0.065727</td>\n",
       "      <td>0.052441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DaysInWaitingList</th>\n",
       "      <td>0.054018</td>\n",
       "      <td>0.170006</td>\n",
       "      <td>-0.056823</td>\n",
       "      <td>0.022992</td>\n",
       "      <td>0.022738</td>\n",
       "      <td>-0.054569</td>\n",
       "      <td>-0.002162</td>\n",
       "      <td>-0.008765</td>\n",
       "      <td>-0.033396</td>\n",
       "      <td>-0.010648</td>\n",
       "      <td>-0.022323</td>\n",
       "      <td>0.005927</td>\n",
       "      <td>-0.009011</td>\n",
       "      <td>-0.011660</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.043269</td>\n",
       "      <td>-0.030463</td>\n",
       "      <td>-0.082971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADR</th>\n",
       "      <td>0.047294</td>\n",
       "      <td>-0.069086</td>\n",
       "      <td>0.207916</td>\n",
       "      <td>0.079992</td>\n",
       "      <td>0.030443</td>\n",
       "      <td>0.050519</td>\n",
       "      <td>0.067530</td>\n",
       "      <td>0.238581</td>\n",
       "      <td>0.341462</td>\n",
       "      <td>0.030090</td>\n",
       "      <td>-0.141962</td>\n",
       "      <td>-0.069117</td>\n",
       "      <td>-0.073024</td>\n",
       "      <td>0.019052</td>\n",
       "      <td>-0.043269</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.061179</td>\n",
       "      <td>0.180665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RequiredCarParkingSpaces</th>\n",
       "      <td>-0.194795</td>\n",
       "      <td>-0.115562</td>\n",
       "      <td>-0.012661</td>\n",
       "      <td>0.001715</td>\n",
       "      <td>0.008265</td>\n",
       "      <td>-0.018149</td>\n",
       "      <td>-0.024379</td>\n",
       "      <td>0.016370</td>\n",
       "      <td>0.057060</td>\n",
       "      <td>0.036970</td>\n",
       "      <td>0.077776</td>\n",
       "      <td>-0.018455</td>\n",
       "      <td>0.046945</td>\n",
       "      <td>0.065727</td>\n",
       "      <td>-0.030463</td>\n",
       "      <td>0.061179</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.082673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotalOfSpecialRequests</th>\n",
       "      <td>-0.235638</td>\n",
       "      <td>-0.096540</td>\n",
       "      <td>0.108926</td>\n",
       "      <td>0.025772</td>\n",
       "      <td>0.003089</td>\n",
       "      <td>0.071664</td>\n",
       "      <td>0.066781</td>\n",
       "      <td>0.121816</td>\n",
       "      <td>0.081784</td>\n",
       "      <td>0.097600</td>\n",
       "      <td>0.013150</td>\n",
       "      <td>-0.048586</td>\n",
       "      <td>0.037595</td>\n",
       "      <td>0.052441</td>\n",
       "      <td>-0.082971</td>\n",
       "      <td>0.180665</td>\n",
       "      <td>0.082673</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             IsCanceled  LeadTime  ArrivalDateYear  \\\n",
       "IsCanceled                     1.000000  0.292004         0.016415   \n",
       "LeadTime                       0.292004  1.000000         0.039923   \n",
       "ArrivalDateYear                0.016415  0.039923         1.000000   \n",
       "ArrivalDateWeekNumber          0.007477  0.126733        -0.540498   \n",
       "ArrivalDateDayOfMonth         -0.006138  0.002325        -0.000588   \n",
       "StaysInWeekendNights          -0.002621  0.083980         0.021668   \n",
       "StaysInWeekNights              0.024119  0.164780         0.031753   \n",
       "Adults                         0.058379  0.116801         0.029147   \n",
       "Children                       0.004754 -0.038336         0.054492   \n",
       "Babies                        -0.032520 -0.021150        -0.013183   \n",
       "IsRepeatedGuest               -0.085178 -0.125093         0.010128   \n",
       "PreviousCancellations          0.109924  0.085960        -0.119922   \n",
       "PreviousBookingsNotCanceled   -0.055493 -0.071128         0.029800   \n",
       "BookingChanges                -0.144674  0.000010         0.031126   \n",
       "DaysInWaitingList              0.054018  0.170006        -0.056823   \n",
       "ADR                            0.047294 -0.069086         0.207916   \n",
       "RequiredCarParkingSpaces      -0.194795 -0.115562        -0.012661   \n",
       "TotalOfSpecialRequests        -0.235638 -0.096540         0.108926   \n",
       "\n",
       "                             ArrivalDateWeekNumber  ArrivalDateDayOfMonth  \\\n",
       "IsCanceled                                0.007477              -0.006138   \n",
       "LeadTime                                  0.126733               0.002325   \n",
       "ArrivalDateYear                          -0.540498              -0.000588   \n",
       "ArrivalDateWeekNumber                     1.000000               0.066849   \n",
       "ArrivalDateDayOfMonth                     0.066849               1.000000   \n",
       "StaysInWeekendNights                      0.017634              -0.015911   \n",
       "StaysInWeekNights                         0.015000              -0.027601   \n",
       "Adults                                    0.025397              -0.001839   \n",
       "Children                                  0.005491               0.014564   \n",
       "Babies                                    0.010038              -0.000539   \n",
       "IsRepeatedGuest                          -0.030413              -0.006339   \n",
       "PreviousCancellations                     0.035366              -0.027012   \n",
       "PreviousBookingsNotCanceled              -0.020769               0.000118   \n",
       "BookingChanges                            0.005197               0.010757   \n",
       "DaysInWaitingList                         0.022992               0.022738   \n",
       "ADR                                       0.079992               0.030443   \n",
       "RequiredCarParkingSpaces                  0.001715               0.008265   \n",
       "TotalOfSpecialRequests                    0.025772               0.003089   \n",
       "\n",
       "                             StaysInWeekendNights  StaysInWeekNights  \\\n",
       "IsCanceled                              -0.002621           0.024119   \n",
       "LeadTime                                 0.083980           0.164780   \n",
       "ArrivalDateYear                          0.021668           0.031753   \n",
       "ArrivalDateWeekNumber                    0.017634           0.015000   \n",
       "ArrivalDateDayOfMonth                   -0.015911          -0.027601   \n",
       "StaysInWeekendNights                     1.000000           0.494885   \n",
       "StaysInWeekNights                        0.494885           1.000000   \n",
       "Adults                                   0.090412           0.092001   \n",
       "Children                                 0.045428           0.044258   \n",
       "Babies                                   0.018396           0.020157   \n",
       "IsRepeatedGuest                         -0.087837          -0.097996   \n",
       "PreviousCancellations                   -0.013009          -0.014274   \n",
       "PreviousBookingsNotCanceled             -0.040598          -0.047368   \n",
       "BookingChanges                           0.062411           0.095673   \n",
       "DaysInWaitingList                       -0.054569          -0.002162   \n",
       "ADR                                      0.050519           0.067530   \n",
       "RequiredCarParkingSpaces                -0.018149          -0.024379   \n",
       "TotalOfSpecialRequests                   0.071664           0.066781   \n",
       "\n",
       "                               Adults  Children    Babies  IsRepeatedGuest  \\\n",
       "IsCanceled                   0.058379  0.004754 -0.032520        -0.085178   \n",
       "LeadTime                     0.116801 -0.038336 -0.021150        -0.125093   \n",
       "ArrivalDateYear              0.029147  0.054492 -0.013183         0.010128   \n",
       "ArrivalDateWeekNumber        0.025397  0.005491  0.010038        -0.030413   \n",
       "ArrivalDateDayOfMonth       -0.001839  0.014564 -0.000539        -0.006339   \n",
       "StaysInWeekendNights         0.090412  0.045428  0.018396        -0.087837   \n",
       "StaysInWeekNights            0.092001  0.044258  0.020157        -0.097996   \n",
       "Adults                       1.000000  0.029591  0.017887        -0.147494   \n",
       "Children                     0.029591  1.000000  0.024130        -0.032990   \n",
       "Babies                       0.017887  0.024130  1.000000        -0.008955   \n",
       "IsRepeatedGuest             -0.147494 -0.032990 -0.008955         1.000000   \n",
       "PreviousCancellations       -0.006974 -0.024753 -0.007490         0.082376   \n",
       "PreviousBookingsNotCanceled -0.105028 -0.020365 -0.006306         0.423259   \n",
       "BookingChanges              -0.052423  0.048663  0.083221         0.012166   \n",
       "DaysInWaitingList           -0.008765 -0.033396 -0.010648        -0.022323   \n",
       "ADR                          0.238581  0.341462  0.030090        -0.141962   \n",
       "RequiredCarParkingSpaces     0.016370  0.057060  0.036970         0.077776   \n",
       "TotalOfSpecialRequests       0.121816  0.081784  0.097600         0.013150   \n",
       "\n",
       "                             PreviousCancellations  \\\n",
       "IsCanceled                                0.109924   \n",
       "LeadTime                                  0.085960   \n",
       "ArrivalDateYear                          -0.119922   \n",
       "ArrivalDateWeekNumber                     0.035366   \n",
       "ArrivalDateDayOfMonth                    -0.027012   \n",
       "StaysInWeekendNights                     -0.013009   \n",
       "StaysInWeekNights                        -0.014274   \n",
       "Adults                                   -0.006974   \n",
       "Children                                 -0.024753   \n",
       "Babies                                   -0.007490   \n",
       "IsRepeatedGuest                           0.082376   \n",
       "PreviousCancellations                     1.000000   \n",
       "PreviousBookingsNotCanceled               0.154285   \n",
       "BookingChanges                           -0.027092   \n",
       "DaysInWaitingList                         0.005927   \n",
       "ADR                                      -0.069117   \n",
       "RequiredCarParkingSpaces                 -0.018455   \n",
       "TotalOfSpecialRequests                   -0.048586   \n",
       "\n",
       "                             PreviousBookingsNotCanceled  BookingChanges  \\\n",
       "IsCanceled                                     -0.055493       -0.144674   \n",
       "LeadTime                                       -0.071128        0.000010   \n",
       "ArrivalDateYear                                 0.029800        0.031126   \n",
       "ArrivalDateWeekNumber                          -0.020769        0.005197   \n",
       "ArrivalDateDayOfMonth                           0.000118        0.010757   \n",
       "StaysInWeekendNights                           -0.040598        0.062411   \n",
       "StaysInWeekNights                              -0.047368        0.095673   \n",
       "Adults                                         -0.105028       -0.052423   \n",
       "Children                                       -0.020365        0.048663   \n",
       "Babies                                         -0.006306        0.083221   \n",
       "IsRepeatedGuest                                 0.423259        0.012166   \n",
       "PreviousCancellations                           0.154285       -0.027092   \n",
       "PreviousBookingsNotCanceled                     1.000000        0.011971   \n",
       "BookingChanges                                  0.011971        1.000000   \n",
       "DaysInWaitingList                              -0.009011       -0.011660   \n",
       "ADR                                            -0.073024        0.019052   \n",
       "RequiredCarParkingSpaces                        0.046945        0.065727   \n",
       "TotalOfSpecialRequests                          0.037595        0.052441   \n",
       "\n",
       "                             DaysInWaitingList       ADR  \\\n",
       "IsCanceled                            0.054018  0.047294   \n",
       "LeadTime                              0.170006 -0.069086   \n",
       "ArrivalDateYear                      -0.056823  0.207916   \n",
       "ArrivalDateWeekNumber                 0.022992  0.079992   \n",
       "ArrivalDateDayOfMonth                 0.022738  0.030443   \n",
       "StaysInWeekendNights                 -0.054569  0.050519   \n",
       "StaysInWeekNights                    -0.002162  0.067530   \n",
       "Adults                               -0.008765  0.238581   \n",
       "Children                             -0.033396  0.341462   \n",
       "Babies                               -0.010648  0.030090   \n",
       "IsRepeatedGuest                      -0.022323 -0.141962   \n",
       "PreviousCancellations                 0.005927 -0.069117   \n",
       "PreviousBookingsNotCanceled          -0.009011 -0.073024   \n",
       "BookingChanges                       -0.011660  0.019052   \n",
       "DaysInWaitingList                     1.000000 -0.043269   \n",
       "ADR                                  -0.043269  1.000000   \n",
       "RequiredCarParkingSpaces             -0.030463  0.061179   \n",
       "TotalOfSpecialRequests               -0.082971  0.180665   \n",
       "\n",
       "                             RequiredCarParkingSpaces  TotalOfSpecialRequests  \n",
       "IsCanceled                                  -0.194795               -0.235638  \n",
       "LeadTime                                    -0.115562               -0.096540  \n",
       "ArrivalDateYear                             -0.012661                0.108926  \n",
       "ArrivalDateWeekNumber                        0.001715                0.025772  \n",
       "ArrivalDateDayOfMonth                        0.008265                0.003089  \n",
       "StaysInWeekendNights                        -0.018149                0.071664  \n",
       "StaysInWeekNights                           -0.024379                0.066781  \n",
       "Adults                                       0.016370                0.121816  \n",
       "Children                                     0.057060                0.081784  \n",
       "Babies                                       0.036970                0.097600  \n",
       "IsRepeatedGuest                              0.077776                0.013150  \n",
       "PreviousCancellations                       -0.018455               -0.048586  \n",
       "PreviousBookingsNotCanceled                  0.046945                0.037595  \n",
       "BookingChanges                               0.065727                0.052441  \n",
       "DaysInWaitingList                           -0.030463               -0.082971  \n",
       "ADR                                          0.061179                0.180665  \n",
       "RequiredCarParkingSpaces                     1.000000                0.082673  \n",
       "TotalOfSpecialRequests                       0.082673                1.000000  "
      ]
     },
     "execution_count": 34,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix # By diagonal entries as feature match with itself thats why its 1.(Full correlated or same)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 435
    },
    "colab_type": "code",
    "id": "v0olq7AcvsWU",
    "outputId": "b9025bdb-3b33-417b-fb71-f96a50190b66"
   },
   "outputs": [
    {
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       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row5_col14 {\n",
       "            background-color:  #4257c9;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row5_col15 {\n",
       "            background-color:  #7093f3;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row5_col16 {\n",
       "            background-color:  #688aef;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row5_col17 {\n",
       "            background-color:  #8caffe;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row6_col0 {\n",
       "            background-color:  #7ea1fa;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row6_col1 {\n",
       "            background-color:  #8fb1fe;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row6_col2 {\n",
       "            background-color:  #b7cff9;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row6_col3 {\n",
       "            background-color:  #b3cdfb;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row6_col4 {\n",
       "            background-color:  #3b4cc0;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row6_col5 {\n",
       "            background-color:  #e6d7cf;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row6_col6 {\n",
       "            background-color:  #b40426;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row6_col7 {\n",
       "            background-color:  #7ea1fa;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row6_col8 {\n",
       "            background-color:  #536edd;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row6_col9 {\n",
       "            background-color:  #4a63d3;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row6_col10 {\n",
       "            background-color:  #485fd1;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row6_col11 {\n",
       "            background-color:  #5875e1;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row6_col12 {\n",
       "            background-color:  #4a63d3;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row6_col13 {\n",
       "            background-color:  #7ea1fa;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row6_col14 {\n",
       "            background-color:  #516ddb;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row6_col15 {\n",
       "            background-color:  #7597f6;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row6_col16 {\n",
       "            background-color:  #6788ee;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row6_col17 {\n",
       "            background-color:  #8badfd;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row7_col0 {\n",
       "            background-color:  #88abfd;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row7_col1 {\n",
       "            background-color:  #81a4fb;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row7_col2 {\n",
       "            background-color:  #b6cefa;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row7_col3 {\n",
       "            background-color:  #b6cefa;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row7_col4 {\n",
       "            background-color:  #4257c9;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row7_col5 {\n",
       "            background-color:  #6e90f2;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row7_col6 {\n",
       "            background-color:  #7295f4;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row7_col7 {\n",
       "            background-color:  #b40426;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row7_col8 {\n",
       "            background-color:  #4e68d8;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row7_col9 {\n",
       "            background-color:  #4961d2;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row7_col10 {\n",
       "            background-color:  #3b4cc0;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row7_col11 {\n",
       "            background-color:  #5977e3;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row7_col12 {\n",
       "            background-color:  #3b4cc0;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row7_col13 {\n",
       "            background-color:  #536edd;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row7_col14 {\n",
       "            background-color:  #4f69d9;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row7_col15 {\n",
       "            background-color:  #aac7fd;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row7_col16 {\n",
       "            background-color:  #7396f5;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row7_col17 {\n",
       "            background-color:  #9bbcff;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row8_col0 {\n",
       "            background-color:  #799cf8;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row8_col1 {\n",
       "            background-color:  #516ddb;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row8_col2 {\n",
       "            background-color:  #bbd1f8;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row8_col3 {\n",
       "            background-color:  #b1cbfc;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row8_col4 {\n",
       "            background-color:  #465ecf;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row8_col5 {\n",
       "            background-color:  #6180e9;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row8_col6 {\n",
       "            background-color:  #6384eb;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row8_col7 {\n",
       "            background-color:  #6b8df0;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row8_col8 {\n",
       "            background-color:  #b40426;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row8_col9 {\n",
       "            background-color:  #4b64d5;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row8_col10 {\n",
       "            background-color:  #5977e3;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row8_col11 {\n",
       "            background-color:  #5470de;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row8_col12 {\n",
       "            background-color:  #516ddb;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row8_col13 {\n",
       "            background-color:  #7093f3;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row8_col14 {\n",
       "            background-color:  #485fd1;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row8_col15 {\n",
       "            background-color:  #c7d7f0;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row8_col16 {\n",
       "            background-color:  #7ea1fa;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row8_col17 {\n",
       "            background-color:  #8fb1fe;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row9_col0 {\n",
       "            background-color:  #6f92f3;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row9_col1 {\n",
       "            background-color:  #5673e0;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row9_col2 {\n",
       "            background-color:  #adc9fd;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row9_col3 {\n",
       "            background-color:  #b2ccfb;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row9_col4 {\n",
       "            background-color:  #4257c9;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row9_col5 {\n",
       "            background-color:  #5875e1;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row9_col6 {\n",
       "            background-color:  #5b7ae5;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row9_col7 {\n",
       "            background-color:  #6788ee;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row9_col8 {\n",
       "            background-color:  #4c66d6;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row9_col9 {\n",
       "            background-color:  #b40426;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row9_col10 {\n",
       "            background-color:  #5f7fe8;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row9_col11 {\n",
       "            background-color:  #5977e3;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row9_col12 {\n",
       "            background-color:  #5572df;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row9_col13 {\n",
       "            background-color:  #7a9df8;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row9_col14 {\n",
       "            background-color:  #4f69d9;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row9_col15 {\n",
       "            background-color:  #6a8bef;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row9_col16 {\n",
       "            background-color:  #799cf8;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row9_col17 {\n",
       "            background-color:  #94b6ff;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row10_col0 {\n",
       "            background-color:  #6180e9;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row10_col1 {\n",
       "            background-color:  #3b4cc0;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row10_col2 {\n",
       "            background-color:  #b2ccfb;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row10_col3 {\n",
       "            background-color:  #a9c6fd;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row10_col4 {\n",
       "            background-color:  #4055c8;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row10_col5 {\n",
       "            background-color:  #3b4cc0;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row10_col6 {\n",
       "            background-color:  #3b4cc0;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row10_col7 {\n",
       "            background-color:  #3b4cc0;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row10_col8 {\n",
       "            background-color:  #3c4ec2;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row10_col9 {\n",
       "            background-color:  #4055c8;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row10_col10 {\n",
       "            background-color:  #b40426;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row10_col11 {\n",
       "            background-color:  #7597f6;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row10_col12 {\n",
       "            background-color:  #d7dce3;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row10_col13 {\n",
       "            background-color:  #6687ed;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row10_col14 {\n",
       "            background-color:  #4b64d5;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row10_col15 {\n",
       "            background-color:  #3b4cc0;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row10_col16 {\n",
       "            background-color:  #85a8fc;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row10_col17 {\n",
       "            background-color:  #7b9ff9;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row11_col0 {\n",
       "            background-color:  #97b8ff;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row11_col1 {\n",
       "            background-color:  #779af7;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row11_col2 {\n",
       "            background-color:  #94b6ff;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row11_col3 {\n",
       "            background-color:  #b7cff9;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row11_col4 {\n",
       "            background-color:  #3b4cc0;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row11_col5 {\n",
       "            background-color:  #4f69d9;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row11_col6 {\n",
       "            background-color:  #516ddb;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row11_col7 {\n",
       "            background-color:  #6180e9;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row11_col8 {\n",
       "            background-color:  #3e51c5;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row11_col9 {\n",
       "            background-color:  #4257c9;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row11_col10 {\n",
       "            background-color:  #7b9ff9;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row11_col11 {\n",
       "            background-color:  #b40426;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row11_col12 {\n",
       "            background-color:  #88abfd;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row11_col13 {\n",
       "            background-color:  #5a78e4;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row11_col14 {\n",
       "            background-color:  #5470de;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row11_col15 {\n",
       "            background-color:  #4e68d8;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row11_col16 {\n",
       "            background-color:  #688aef;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row11_col17 {\n",
       "            background-color:  #6a8bef;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row12_col0 {\n",
       "            background-color:  #688aef;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row12_col1 {\n",
       "            background-color:  #4961d2;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row12_col2 {\n",
       "            background-color:  #b6cefa;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row12_col3 {\n",
       "            background-color:  #abc8fd;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row12_col4 {\n",
       "            background-color:  #4257c9;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row12_col5 {\n",
       "            background-color:  #485fd1;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row12_col6 {\n",
       "            background-color:  #485fd1;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row12_col7 {\n",
       "            background-color:  #455cce;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row12_col8 {\n",
       "            background-color:  #3f53c6;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row12_col9 {\n",
       "            background-color:  #4257c9;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row12_col10 {\n",
       "            background-color:  #dcdddd;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row12_col11 {\n",
       "            background-color:  #8badfd;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row12_col12 {\n",
       "            background-color:  #b40426;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row12_col13 {\n",
       "            background-color:  #6687ed;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row12_col14 {\n",
       "            background-color:  #4f69d9;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row12_col15 {\n",
       "            background-color:  #4c66d6;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row12_col16 {\n",
       "            background-color:  #7b9ff9;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row12_col17 {\n",
       "            background-color:  #82a6fb;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row13_col0 {\n",
       "            background-color:  #506bda;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row13_col1 {\n",
       "            background-color:  #5d7ce6;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row13_col2 {\n",
       "            background-color:  #b6cefa;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row13_col3 {\n",
       "            background-color:  #b1cbfc;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row13_col4 {\n",
       "            background-color:  #455cce;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row13_col5 {\n",
       "            background-color:  #6687ed;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row13_col6 {\n",
       "            background-color:  #7396f5;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row13_col7 {\n",
       "            background-color:  #5470de;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row13_col8 {\n",
       "            background-color:  #5470de;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row13_col9 {\n",
       "            background-color:  #5d7ce6;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row13_col10 {\n",
       "            background-color:  #6687ed;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row13_col11 {\n",
       "            background-color:  #5470de;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row13_col12 {\n",
       "            background-color:  #5b7ae5;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row13_col13 {\n",
       "            background-color:  #b40426;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row13_col14 {\n",
       "            background-color:  #4e68d8;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row13_col15 {\n",
       "            background-color:  #6788ee;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row13_col16 {\n",
       "            background-color:  #81a4fb;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row13_col17 {\n",
       "            background-color:  #86a9fc;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row14_col0 {\n",
       "            background-color:  #88abfd;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row14_col1 {\n",
       "            background-color:  #92b4fe;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row14_col2 {\n",
       "            background-color:  #a3c2fe;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row14_col3 {\n",
       "            background-color:  #b5cdfa;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row14_col4 {\n",
       "            background-color:  #4961d2;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row14_col5 {\n",
       "            background-color:  #4358cb;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row14_col6 {\n",
       "            background-color:  #5572df;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row14_col7 {\n",
       "            background-color:  #5f7fe8;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row14_col8 {\n",
       "            background-color:  #3c4ec2;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row14_col9 {\n",
       "            background-color:  #4055c8;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row14_col10 {\n",
       "            background-color:  #5b7ae5;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row14_col11 {\n",
       "            background-color:  #5d7ce6;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row14_col12 {\n",
       "            background-color:  #5572df;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row14_col13 {\n",
       "            background-color:  #5e7de7;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row14_col14 {\n",
       "            background-color:  #b40426;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row14_col15 {\n",
       "            background-color:  #5572df;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row14_col16 {\n",
       "            background-color:  #6687ed;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row14_col17 {\n",
       "            background-color:  #6180e9;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row15_col0 {\n",
       "            background-color:  #85a8fc;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row15_col1 {\n",
       "            background-color:  #4961d2;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row15_col2 {\n",
       "            background-color:  #d9dce1;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row15_col3 {\n",
       "            background-color:  #c1d4f4;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row15_col4 {\n",
       "            background-color:  #4b64d5;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row15_col5 {\n",
       "            background-color:  #6282ea;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row15_col6 {\n",
       "            background-color:  #6a8bef;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row15_col7 {\n",
       "            background-color:  #abc8fd;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row15_col8 {\n",
       "            background-color:  #b5cdfa;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row15_col9 {\n",
       "            background-color:  #4c66d6;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row15_col10 {\n",
       "            background-color:  #3c4ec2;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row15_col11 {\n",
       "            background-color:  #485fd1;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row15_col12 {\n",
       "            background-color:  #4358cb;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row15_col13 {\n",
       "            background-color:  #6788ee;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row15_col14 {\n",
       "            background-color:  #455cce;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row15_col15 {\n",
       "            background-color:  #b40426;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row15_col16 {\n",
       "            background-color:  #80a3fa;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row15_col17 {\n",
       "            background-color:  #abc8fd;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row16_col0 {\n",
       "            background-color:  #445acc;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row16_col1 {\n",
       "            background-color:  #3d50c3;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row16_col2 {\n",
       "            background-color:  #adc9fd;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row16_col3 {\n",
       "            background-color:  #b1cbfc;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row16_col4 {\n",
       "            background-color:  #445acc;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row16_col5 {\n",
       "            background-color:  #4e68d8;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row16_col6 {\n",
       "            background-color:  #4f69d9;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row16_col7 {\n",
       "            background-color:  #6788ee;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row16_col8 {\n",
       "            background-color:  #5673e0;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row16_col9 {\n",
       "            background-color:  #4f69d9;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row16_col10 {\n",
       "            background-color:  #7a9df8;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row16_col11 {\n",
       "            background-color:  #5673e0;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row16_col12 {\n",
       "            background-color:  #6687ed;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row16_col13 {\n",
       "            background-color:  #7699f6;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row16_col14 {\n",
       "            background-color:  #4961d2;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row16_col15 {\n",
       "            background-color:  #7396f5;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row16_col16 {\n",
       "            background-color:  #b40426;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row16_col17 {\n",
       "            background-color:  #8fb1fe;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row17_col0 {\n",
       "            background-color:  #3b4cc0;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row17_col1 {\n",
       "            background-color:  #4257c9;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row17_col2 {\n",
       "            background-color:  #c6d6f1;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row17_col3 {\n",
       "            background-color:  #b6cefa;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row17_col4 {\n",
       "            background-color:  #4358cb;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row17_col5 {\n",
       "            background-color:  #688aef;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row17_col6 {\n",
       "            background-color:  #6a8bef;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row17_col7 {\n",
       "            background-color:  #88abfd;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row17_col8 {\n",
       "            background-color:  #5e7de7;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row17_col9 {\n",
       "            background-color:  #6282ea;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row17_col10 {\n",
       "            background-color:  #6687ed;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row17_col11 {\n",
       "            background-color:  #4e68d8;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row17_col12 {\n",
       "            background-color:  #6384eb;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row17_col13 {\n",
       "            background-color:  #7295f4;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row17_col14 {\n",
       "            background-color:  #3b4cc0;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row17_col15 {\n",
       "            background-color:  #98b9ff;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row17_col16 {\n",
       "            background-color:  #86a9fc;\n",
       "            color:  #000000;\n",
       "        }    #T_e5ab737a_68e6_11e9_b847_0242ac1c0002row17_col17 {\n",
       "            background-color:  #b40426;\n",
       "            color:  #f1f1f1;\n",
       "        }</style><table id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >IsCanceled</th>        <th class=\"col_heading level0 col1\" >LeadTime</th>        <th class=\"col_heading level0 col2\" >ArrivalDateYear</th>        <th class=\"col_heading level0 col3\" >ArrivalDateWeekNumber</th>        <th class=\"col_heading level0 col4\" >ArrivalDateDayOfMonth</th>        <th class=\"col_heading level0 col5\" >StaysInWeekendNights</th>        <th class=\"col_heading level0 col6\" >StaysInWeekNights</th>        <th class=\"col_heading level0 col7\" >Adults</th>        <th class=\"col_heading level0 col8\" >Children</th>        <th class=\"col_heading level0 col9\" >Babies</th>        <th class=\"col_heading level0 col10\" >IsRepeatedGuest</th>        <th class=\"col_heading level0 col11\" >PreviousCancellations</th>        <th class=\"col_heading level0 col12\" >PreviousBookingsNotCanceled</th>        <th class=\"col_heading level0 col13\" >BookingChanges</th>        <th class=\"col_heading level0 col14\" >DaysInWaitingList</th>        <th class=\"col_heading level0 col15\" >ADR</th>        <th class=\"col_heading level0 col16\" >RequiredCarParkingSpaces</th>        <th class=\"col_heading level0 col17\" >TotalOfSpecialRequests</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >IsCanceled</th>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row0_col0\" class=\"data row0 col0\" >1</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row0_col1\" class=\"data row0 col1\" >0.292004</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row0_col2\" class=\"data row0 col2\" >0.0164145</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row0_col3\" class=\"data row0 col3\" >0.00747668</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row0_col4\" class=\"data row0 col4\" >-0.00613804</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row0_col5\" class=\"data row0 col5\" >-0.00262116</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row0_col6\" class=\"data row0 col6\" >0.0241186</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row0_col7\" class=\"data row0 col7\" >0.058379</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row0_col8\" class=\"data row0 col8\" >0.00475402</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row0_col9\" class=\"data row0 col9\" >-0.0325199</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row0_col10\" class=\"data row0 col10\" >-0.0851779</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row0_col11\" class=\"data row0 col11\" >0.109924</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row0_col12\" class=\"data row0 col12\" >-0.0554926</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row0_col13\" class=\"data row0 col13\" >-0.144674</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row0_col14\" class=\"data row0 col14\" >0.0540175</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row0_col15\" class=\"data row0 col15\" >0.0472944</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row0_col16\" class=\"data row0 col16\" >-0.194795</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row0_col17\" class=\"data row0 col17\" >-0.235638</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >LeadTime</th>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row1_col0\" class=\"data row1 col0\" >0.292004</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row1_col1\" class=\"data row1 col1\" >1</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row1_col2\" class=\"data row1 col2\" >0.0399227</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row1_col3\" class=\"data row1 col3\" >0.126733</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row1_col4\" class=\"data row1 col4\" >0.00232525</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row1_col5\" class=\"data row1 col5\" >0.0839795</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row1_col6\" class=\"data row1 col6\" >0.16478</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row1_col7\" class=\"data row1 col7\" >0.116801</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row1_col8\" class=\"data row1 col8\" >-0.0383364</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row1_col9\" class=\"data row1 col9\" >-0.0211496</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row1_col10\" class=\"data row1 col10\" >-0.125093</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row1_col11\" class=\"data row1 col11\" >0.0859601</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row1_col12\" class=\"data row1 col12\" >-0.0711282</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row1_col13\" class=\"data row1 col13\" >1.01075e-05</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row1_col14\" class=\"data row1 col14\" >0.170006</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row1_col15\" class=\"data row1 col15\" >-0.0690864</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row1_col16\" class=\"data row1 col16\" >-0.115562</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row1_col17\" class=\"data row1 col17\" >-0.0965399</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >ArrivalDateYear</th>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row2_col0\" class=\"data row2 col0\" >0.0164145</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row2_col1\" class=\"data row2 col1\" >0.0399227</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row2_col2\" class=\"data row2 col2\" >1</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row2_col3\" class=\"data row2 col3\" >-0.540498</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row2_col4\" class=\"data row2 col4\" >-0.000587692</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row2_col5\" class=\"data row2 col5\" >0.0216678</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row2_col6\" class=\"data row2 col6\" >0.0317525</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row2_col7\" class=\"data row2 col7\" >0.0291468</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row2_col8\" class=\"data row2 col8\" >0.0544916</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row2_col9\" class=\"data row2 col9\" >-0.0131834</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row2_col10\" class=\"data row2 col10\" >0.0101276</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row2_col11\" class=\"data row2 col11\" >-0.119922</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row2_col12\" class=\"data row2 col12\" >0.0298</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row2_col13\" class=\"data row2 col13\" >0.031126</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row2_col14\" class=\"data row2 col14\" >-0.056823</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row2_col15\" class=\"data row2 col15\" >0.207916</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row2_col16\" class=\"data row2 col16\" >-0.0126613</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row2_col17\" class=\"data row2 col17\" >0.108926</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >ArrivalDateWeekNumber</th>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row3_col0\" class=\"data row3 col0\" >0.00747668</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row3_col1\" class=\"data row3 col1\" >0.126733</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row3_col2\" class=\"data row3 col2\" >-0.540498</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row3_col3\" class=\"data row3 col3\" >1</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row3_col4\" class=\"data row3 col4\" >0.0668487</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row3_col5\" class=\"data row3 col5\" >0.0176339</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row3_col6\" class=\"data row3 col6\" >0.0150005</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row3_col7\" class=\"data row3 col7\" >0.0253966</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row3_col8\" class=\"data row3 col8\" >0.00549149</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row3_col9\" class=\"data row3 col9\" >0.0100383</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row3_col10\" class=\"data row3 col10\" >-0.0304132</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row3_col11\" class=\"data row3 col11\" >0.0353663</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row3_col12\" class=\"data row3 col12\" >-0.0207691</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row3_col13\" class=\"data row3 col13\" >0.00519736</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row3_col14\" class=\"data row3 col14\" >0.0229924</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row3_col15\" class=\"data row3 col15\" >0.079992</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row3_col16\" class=\"data row3 col16\" >0.00171498</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row3_col17\" class=\"data row3 col17\" >0.0257719</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >ArrivalDateDayOfMonth</th>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row4_col0\" class=\"data row4 col0\" >-0.00613804</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row4_col1\" class=\"data row4 col1\" >0.00232525</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row4_col2\" class=\"data row4 col2\" >-0.000587692</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row4_col3\" class=\"data row4 col3\" >0.0668487</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row4_col4\" class=\"data row4 col4\" >1</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row4_col5\" class=\"data row4 col5\" >-0.0159114</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row4_col6\" class=\"data row4 col6\" >-0.0276013</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row4_col7\" class=\"data row4 col7\" >-0.00183921</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row4_col8\" class=\"data row4 col8\" >0.014564</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row4_col9\" class=\"data row4 col9\" >-0.000538641</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row4_col10\" class=\"data row4 col10\" >-0.00633881</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row4_col11\" class=\"data row4 col11\" >-0.0270124</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row4_col12\" class=\"data row4 col12\" >0.000118485</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row4_col13\" class=\"data row4 col13\" >0.0107566</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row4_col14\" class=\"data row4 col14\" >0.0227382</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row4_col15\" class=\"data row4 col15\" >0.0304426</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row4_col16\" class=\"data row4 col16\" >0.00826468</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row4_col17\" class=\"data row4 col17\" >0.0030886</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >StaysInWeekendNights</th>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row5_col0\" class=\"data row5 col0\" >-0.00262116</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row5_col1\" class=\"data row5 col1\" >0.0839795</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row5_col2\" class=\"data row5 col2\" >0.0216678</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row5_col3\" class=\"data row5 col3\" >0.0176339</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row5_col4\" class=\"data row5 col4\" >-0.0159114</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row5_col5\" class=\"data row5 col5\" >1</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row5_col6\" class=\"data row5 col6\" >0.494885</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row5_col7\" class=\"data row5 col7\" >0.0904121</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row5_col8\" class=\"data row5 col8\" >0.0454281</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row5_col9\" class=\"data row5 col9\" >0.0183959</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row5_col10\" class=\"data row5 col10\" >-0.0878373</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row5_col11\" class=\"data row5 col11\" >-0.0130091</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row5_col12\" class=\"data row5 col12\" >-0.0405977</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row5_col13\" class=\"data row5 col13\" >0.0624108</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row5_col14\" class=\"data row5 col14\" >-0.0545691</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row5_col15\" class=\"data row5 col15\" >0.0505193</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row5_col16\" class=\"data row5 col16\" >-0.0181486</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row5_col17\" class=\"data row5 col17\" >0.0716635</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >StaysInWeekNights</th>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row6_col0\" class=\"data row6 col0\" >0.0241186</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row6_col1\" class=\"data row6 col1\" >0.16478</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row6_col2\" class=\"data row6 col2\" >0.0317525</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row6_col3\" class=\"data row6 col3\" >0.0150005</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row6_col4\" class=\"data row6 col4\" >-0.0276013</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row6_col5\" class=\"data row6 col5\" >0.494885</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row6_col6\" class=\"data row6 col6\" >1</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row6_col7\" class=\"data row6 col7\" >0.0920011</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row6_col8\" class=\"data row6 col8\" >0.0442577</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row6_col9\" class=\"data row6 col9\" >0.0201569</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row6_col10\" class=\"data row6 col10\" >-0.0979959</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row6_col11\" class=\"data row6 col11\" >-0.0142743</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row6_col12\" class=\"data row6 col12\" >-0.0473679</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row6_col13\" class=\"data row6 col13\" >0.0956733</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row6_col14\" class=\"data row6 col14\" >-0.00216183</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row6_col15\" class=\"data row6 col15\" >0.0675296</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row6_col16\" class=\"data row6 col16\" >-0.0243794</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row6_col17\" class=\"data row6 col17\" >0.0667806</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002level0_row7\" class=\"row_heading level0 row7\" >Adults</th>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row7_col0\" class=\"data row7 col0\" >0.058379</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row7_col1\" class=\"data row7 col1\" >0.116801</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row7_col2\" class=\"data row7 col2\" >0.0291468</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row7_col3\" class=\"data row7 col3\" >0.0253966</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row7_col4\" class=\"data row7 col4\" >-0.00183921</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row7_col5\" class=\"data row7 col5\" >0.0904121</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row7_col6\" class=\"data row7 col6\" >0.0920011</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row7_col7\" class=\"data row7 col7\" >1</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row7_col8\" class=\"data row7 col8\" >0.0295909</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row7_col9\" class=\"data row7 col9\" >0.0178868</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row7_col10\" class=\"data row7 col10\" >-0.147494</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row7_col11\" class=\"data row7 col11\" >-0.00697358</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row7_col12\" class=\"data row7 col12\" >-0.105028</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row7_col13\" class=\"data row7 col13\" >-0.0524232</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row7_col14\" class=\"data row7 col14\" >-0.00876454</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row7_col15\" class=\"data row7 col15\" >0.238581</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row7_col16\" class=\"data row7 col16\" >0.0163701</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row7_col17\" class=\"data row7 col17\" >0.121816</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002level0_row8\" class=\"row_heading level0 row8\" >Children</th>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row8_col0\" class=\"data row8 col0\" >0.00475402</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row8_col1\" class=\"data row8 col1\" >-0.0383364</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row8_col2\" class=\"data row8 col2\" >0.0544916</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row8_col3\" class=\"data row8 col3\" >0.00549149</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row8_col4\" class=\"data row8 col4\" >0.014564</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row8_col5\" class=\"data row8 col5\" >0.0454281</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row8_col6\" class=\"data row8 col6\" >0.0442577</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row8_col7\" class=\"data row8 col7\" >0.0295909</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row8_col8\" class=\"data row8 col8\" >1</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row8_col9\" class=\"data row8 col9\" >0.0241304</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row8_col10\" class=\"data row8 col10\" >-0.0329904</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row8_col11\" class=\"data row8 col11\" >-0.0247525</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row8_col12\" class=\"data row8 col12\" >-0.0203647</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row8_col13\" class=\"data row8 col13\" >0.0486632</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row8_col14\" class=\"data row8 col14\" >-0.0333963</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row8_col15\" class=\"data row8 col15\" >0.341462</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row8_col16\" class=\"data row8 col16\" >0.0570599</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row8_col17\" class=\"data row8 col17\" >0.0817843</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002level0_row9\" class=\"row_heading level0 row9\" >Babies</th>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row9_col0\" class=\"data row9 col0\" >-0.0325199</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row9_col1\" class=\"data row9 col1\" >-0.0211496</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row9_col2\" class=\"data row9 col2\" >-0.0131834</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row9_col3\" class=\"data row9 col3\" >0.0100383</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row9_col4\" class=\"data row9 col4\" >-0.000538641</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row9_col5\" class=\"data row9 col5\" >0.0183959</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row9_col6\" class=\"data row9 col6\" >0.0201569</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row9_col7\" class=\"data row9 col7\" >0.0178868</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row9_col8\" class=\"data row9 col8\" >0.0241304</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row9_col9\" class=\"data row9 col9\" >1</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row9_col10\" class=\"data row9 col10\" >-0.00895456</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row9_col11\" class=\"data row9 col11\" >-0.00748953</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row9_col12\" class=\"data row9 col12\" >-0.00630576</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row9_col13\" class=\"data row9 col13\" >0.0832213</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row9_col14\" class=\"data row9 col14\" >-0.0106481</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row9_col15\" class=\"data row9 col15\" >0.0300901</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row9_col16\" class=\"data row9 col16\" >0.0369704</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row9_col17\" class=\"data row9 col17\" >0.0976004</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002level0_row10\" class=\"row_heading level0 row10\" >IsRepeatedGuest</th>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row10_col0\" class=\"data row10 col0\" >-0.0851779</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row10_col1\" class=\"data row10 col1\" >-0.125093</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row10_col2\" class=\"data row10 col2\" >0.0101276</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row10_col3\" class=\"data row10 col3\" >-0.0304132</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row10_col4\" class=\"data row10 col4\" >-0.00633881</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row10_col5\" class=\"data row10 col5\" >-0.0878373</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row10_col6\" class=\"data row10 col6\" >-0.0979959</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row10_col7\" class=\"data row10 col7\" >-0.147494</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row10_col8\" class=\"data row10 col8\" >-0.0329904</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row10_col9\" class=\"data row10 col9\" >-0.00895456</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row10_col10\" class=\"data row10 col10\" >1</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row10_col11\" class=\"data row10 col11\" >0.0823757</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row10_col12\" class=\"data row10 col12\" >0.423259</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row10_col13\" class=\"data row10 col13\" >0.0121656</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row10_col14\" class=\"data row10 col14\" >-0.0223227</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row10_col15\" class=\"data row10 col15\" >-0.141962</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row10_col16\" class=\"data row10 col16\" >0.0777761</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row10_col17\" class=\"data row10 col17\" >0.0131497</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002level0_row11\" class=\"row_heading level0 row11\" >PreviousCancellations</th>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row11_col0\" class=\"data row11 col0\" >0.109924</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row11_col1\" class=\"data row11 col1\" >0.0859601</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row11_col2\" class=\"data row11 col2\" >-0.119922</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row11_col3\" class=\"data row11 col3\" >0.0353663</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row11_col4\" class=\"data row11 col4\" >-0.0270124</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row11_col5\" class=\"data row11 col5\" >-0.0130091</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row11_col6\" class=\"data row11 col6\" >-0.0142743</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row11_col7\" class=\"data row11 col7\" >-0.00697358</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row11_col8\" class=\"data row11 col8\" >-0.0247525</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row11_col9\" class=\"data row11 col9\" >-0.00748953</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row11_col10\" class=\"data row11 col10\" >0.0823757</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row11_col11\" class=\"data row11 col11\" >1</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row11_col12\" class=\"data row11 col12\" >0.154285</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row11_col13\" class=\"data row11 col13\" >-0.0270916</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row11_col14\" class=\"data row11 col14\" >0.00592677</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row11_col15\" class=\"data row11 col15\" >-0.0691174</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row11_col16\" class=\"data row11 col16\" >-0.0184551</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row11_col17\" class=\"data row11 col17\" >-0.0485855</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002level0_row12\" class=\"row_heading level0 row12\" >PreviousBookingsNotCanceled</th>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row12_col0\" class=\"data row12 col0\" >-0.0554926</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row12_col1\" class=\"data row12 col1\" >-0.0711282</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row12_col2\" class=\"data row12 col2\" >0.0298</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row12_col3\" class=\"data row12 col3\" >-0.0207691</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row12_col4\" class=\"data row12 col4\" >0.000118485</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row12_col5\" class=\"data row12 col5\" >-0.0405977</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row12_col6\" class=\"data row12 col6\" >-0.0473679</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row12_col7\" class=\"data row12 col7\" >-0.105028</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row12_col8\" class=\"data row12 col8\" >-0.0203647</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row12_col9\" class=\"data row12 col9\" >-0.00630576</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row12_col10\" class=\"data row12 col10\" >0.423259</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row12_col11\" class=\"data row12 col11\" >0.154285</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row12_col12\" class=\"data row12 col12\" >1</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row12_col13\" class=\"data row12 col13\" >0.0119707</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row12_col14\" class=\"data row12 col14\" >-0.00901135</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row12_col15\" class=\"data row12 col15\" >-0.0730236</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row12_col16\" class=\"data row12 col16\" >0.0469451</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row12_col17\" class=\"data row12 col17\" >0.037595</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002level0_row13\" class=\"row_heading level0 row13\" >BookingChanges</th>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row13_col0\" class=\"data row13 col0\" >-0.144674</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row13_col1\" class=\"data row13 col1\" >1.01075e-05</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row13_col2\" class=\"data row13 col2\" >0.031126</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row13_col3\" class=\"data row13 col3\" >0.00519736</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row13_col4\" class=\"data row13 col4\" >0.0107566</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row13_col5\" class=\"data row13 col5\" >0.0624108</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row13_col6\" class=\"data row13 col6\" >0.0956733</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row13_col7\" class=\"data row13 col7\" >-0.0524232</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row13_col8\" class=\"data row13 col8\" >0.0486632</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row13_col9\" class=\"data row13 col9\" >0.0832213</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row13_col10\" class=\"data row13 col10\" >0.0121656</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row13_col11\" class=\"data row13 col11\" >-0.0270916</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row13_col12\" class=\"data row13 col12\" >0.0119707</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row13_col13\" class=\"data row13 col13\" >1</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row13_col14\" class=\"data row13 col14\" >-0.0116602</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row13_col15\" class=\"data row13 col15\" >0.0190523</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row13_col16\" class=\"data row13 col16\" >0.0657269</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row13_col17\" class=\"data row13 col17\" >0.0524412</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002level0_row14\" class=\"row_heading level0 row14\" >DaysInWaitingList</th>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row14_col0\" class=\"data row14 col0\" >0.0540175</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row14_col1\" class=\"data row14 col1\" >0.170006</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row14_col2\" class=\"data row14 col2\" >-0.056823</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row14_col3\" class=\"data row14 col3\" >0.0229924</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row14_col4\" class=\"data row14 col4\" >0.0227382</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row14_col5\" class=\"data row14 col5\" >-0.0545691</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row14_col6\" class=\"data row14 col6\" >-0.00216183</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row14_col7\" class=\"data row14 col7\" >-0.00876454</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row14_col8\" class=\"data row14 col8\" >-0.0333963</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row14_col9\" class=\"data row14 col9\" >-0.0106481</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row14_col10\" class=\"data row14 col10\" >-0.0223227</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row14_col11\" class=\"data row14 col11\" >0.00592677</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row14_col12\" class=\"data row14 col12\" >-0.00901135</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row14_col13\" class=\"data row14 col13\" >-0.0116602</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row14_col14\" class=\"data row14 col14\" >1</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row14_col15\" class=\"data row14 col15\" >-0.0432691</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row14_col16\" class=\"data row14 col16\" >-0.0304626</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row14_col17\" class=\"data row14 col17\" >-0.0829707</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002level0_row15\" class=\"row_heading level0 row15\" >ADR</th>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row15_col0\" class=\"data row15 col0\" >0.0472944</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row15_col1\" class=\"data row15 col1\" >-0.0690864</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row15_col2\" class=\"data row15 col2\" >0.207916</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row15_col3\" class=\"data row15 col3\" >0.079992</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row15_col4\" class=\"data row15 col4\" >0.0304426</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row15_col5\" class=\"data row15 col5\" >0.0505193</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row15_col6\" class=\"data row15 col6\" >0.0675296</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row15_col7\" class=\"data row15 col7\" >0.238581</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row15_col8\" class=\"data row15 col8\" >0.341462</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row15_col9\" class=\"data row15 col9\" >0.0300901</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row15_col10\" class=\"data row15 col10\" >-0.141962</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row15_col11\" class=\"data row15 col11\" >-0.0691174</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row15_col12\" class=\"data row15 col12\" >-0.0730236</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row15_col13\" class=\"data row15 col13\" >0.0190523</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row15_col14\" class=\"data row15 col14\" >-0.0432691</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row15_col15\" class=\"data row15 col15\" >1</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row15_col16\" class=\"data row15 col16\" >0.0611786</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row15_col17\" class=\"data row15 col17\" >0.180665</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002level0_row16\" class=\"row_heading level0 row16\" >RequiredCarParkingSpaces</th>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row16_col0\" class=\"data row16 col0\" >-0.194795</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row16_col1\" class=\"data row16 col1\" >-0.115562</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row16_col2\" class=\"data row16 col2\" >-0.0126613</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row16_col3\" class=\"data row16 col3\" >0.00171498</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row16_col4\" class=\"data row16 col4\" >0.00826468</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row16_col5\" class=\"data row16 col5\" >-0.0181486</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row16_col6\" class=\"data row16 col6\" >-0.0243794</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row16_col7\" class=\"data row16 col7\" >0.0163701</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row16_col8\" class=\"data row16 col8\" >0.0570599</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row16_col9\" class=\"data row16 col9\" >0.0369704</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row16_col10\" class=\"data row16 col10\" >0.0777761</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row16_col11\" class=\"data row16 col11\" >-0.0184551</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row16_col12\" class=\"data row16 col12\" >0.0469451</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row16_col13\" class=\"data row16 col13\" >0.0657269</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row16_col14\" class=\"data row16 col14\" >-0.0304626</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row16_col15\" class=\"data row16 col15\" >0.0611786</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row16_col16\" class=\"data row16 col16\" >1</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row16_col17\" class=\"data row16 col17\" >0.0826734</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002level0_row17\" class=\"row_heading level0 row17\" >TotalOfSpecialRequests</th>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row17_col0\" class=\"data row17 col0\" >-0.235638</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row17_col1\" class=\"data row17 col1\" >-0.0965399</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row17_col2\" class=\"data row17 col2\" >0.108926</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row17_col3\" class=\"data row17 col3\" >0.0257719</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row17_col4\" class=\"data row17 col4\" >0.0030886</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row17_col5\" class=\"data row17 col5\" >0.0716635</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row17_col6\" class=\"data row17 col6\" >0.0667806</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row17_col7\" class=\"data row17 col7\" >0.121816</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row17_col8\" class=\"data row17 col8\" >0.0817843</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row17_col9\" class=\"data row17 col9\" >0.0976004</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row17_col10\" class=\"data row17 col10\" >0.0131497</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row17_col11\" class=\"data row17 col11\" >-0.0485855</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row17_col12\" class=\"data row17 col12\" >0.037595</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row17_col13\" class=\"data row17 col13\" >0.0524412</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row17_col14\" class=\"data row17 col14\" >-0.0829707</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row17_col15\" class=\"data row17 col15\" >0.180665</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row17_col16\" class=\"data row17 col16\" >0.0826734</td>\n",
       "                        <td id=\"T_e5ab737a_68e6_11e9_b847_0242ac1c0002row17_col17\" class=\"data row17 col17\" >1</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f6e533d3da0>"
      ]
     },
     "execution_count": 35,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix.style.background_gradient(cmap='coolwarm') # the more white the more correlated, the more blue the less correlated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "SPjhUwg6rmHy"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "tu_QmGkfi196"
   },
   "source": [
    "### Exercise 5\n",
    "\n",
    "Suppose we are interested in the ADR feature. Further investigate the relationship between the ADR and some of the other features that you think might be relevant. What do you notice? Feel free to construct new features or modify existing ones if you think it would enhance the analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vpPfS_8cTo8Z"
   },
   "source": [
    "ADR feature is highly correlated with these features. Children, Adults, ArrivalDateYear, TotalOfSpecialRequests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 336
    },
    "colab_type": "code",
    "id": "xbAr_Acas1MZ",
    "outputId": "b6836b7b-3f04-4399-c4a7-c5ae642b8f7b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ADR                            1.000000\n",
      "Children                       0.341462\n",
      "Adults                         0.238581\n",
      "ArrivalDateYear                0.207916\n",
      "TotalOfSpecialRequests         0.180665\n",
      "ArrivalDateWeekNumber          0.079992\n",
      "StaysInWeekNights              0.067530\n",
      "RequiredCarParkingSpaces       0.061179\n",
      "StaysInWeekendNights           0.050519\n",
      "IsCanceled                     0.047294\n",
      "ArrivalDateDayOfMonth          0.030443\n",
      "Babies                         0.030090\n",
      "BookingChanges                 0.019052\n",
      "DaysInWaitingList             -0.043269\n",
      "LeadTime                      -0.069086\n",
      "PreviousCancellations         -0.069117\n",
      "PreviousBookingsNotCanceled   -0.073024\n",
      "IsRepeatedGuest               -0.141962\n",
      "Name: ADR, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(corr_matrix['ADR'].sort_values(ascending=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "aT3lXhmgi197"
   },
   "source": [
    "## Part 3: Machine Learning and Data Modeling\n",
    "\n",
    "In this section, we will touch on some advanced data science tools to solve some realistic problems."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "YWtKh_N7i198"
   },
   "source": [
    "### Exercise 1\n",
    "\n",
    "Deploy a linear machine learning model that predicts the ADR as a function of whatever single feature you think is most strongly correlated. Are you able to get a good prediction stats on your validation set? Why or why not?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "myKQXDLWoKOv"
   },
   "source": [
    "1(a). Applying linear regression model between **children** features and **ADR** as they are correlated highly. The accuracy on test set is very low because the features wide spreading doesn't cover by linear regression line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 50
    },
    "colab_type": "code",
    "id": "WwFD4VcgjMpO",
    "outputId": "73fa8b68-b74f-40e0-dd74-4b4c1483f197"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set score: 0.12\n",
      "Test set score: 0.11\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "X=H_dataset['Children'].values\n",
    "y=H_dataset['ADR'].values\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)\n",
    "X_train=X_train.reshape(-1,1)\n",
    "X_test=X_test.reshape(-1,1)\n",
    "\n",
    "lr = LinearRegression().fit(X_train, y_train)\n",
    "score_training = lr.score(X_train, y_train)\n",
    "score_test = lr.score(X_test, y_test)\n",
    "\n",
    "\n",
    "print(\"Training set score: {:.2f}\".format(score_training))\n",
    "print(\"Test set score: {:.2f}\".format(score_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "abhjXvPwo-lh"
   },
   "source": [
    "1(b). Applying linear regression model between **TotalOfSpecialRequests ** features and **ADR** as they are correlated highly. The accuracy on test set is very low because the features wide spreading doesn't cover by linear regression line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 50
    },
    "colab_type": "code",
    "id": "7o1Y9eE4mEhm",
    "outputId": "06ce19c2-4323-4596-bca8-6dfabbbfe74c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set score: 0.03\n",
      "Test set score: 0.03\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "X=H_dataset['TotalOfSpecialRequests'].values\n",
    "y=H_dataset['ADR'].values\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)\n",
    "X_train=X_train.reshape(-1,1)\n",
    "X_test=X_test.reshape(-1,1)\n",
    "\n",
    "lr = LinearRegression().fit(X_train, y_train)\n",
    "score_training = lr.score(X_train, y_train)\n",
    "score_test = lr.score(X_test, y_test)\n",
    "\n",
    "\n",
    "print(\"Training set score: {:.2f}\".format(score_training))\n",
    "print(\"Test set score: {:.2f}\".format(score_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "hnHlVJJXpbIl"
   },
   "source": [
    "Ridge  regression are some of the simple techniques to reduce model complexity and prevent over-fitting which may result from simple linear regression.\n",
    "Lets apply them on our dataset\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ODG7IbPJp9zB"
   },
   "source": [
    "2(a). Ridge Regression between Children features and ADR. its l2 reglarization but the results are same so model not overfiting  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 50
    },
    "colab_type": "code",
    "id": "SDjWqc4NnAtk",
    "outputId": "b873b0a5-99a8-45e7-82d2-2a540a00dd70"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set score: 0.12\n",
      "Test set score: 0.11\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "X=H_dataset['Children'].values\n",
    "y=H_dataset['ADR'].values\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)\n",
    "X_train=X_train.reshape(-1,1)\n",
    "X_test=X_test.reshape(-1,1)\n",
    "\n",
    "\n",
    "ridge = Ridge().fit(X_train, y_train)\n",
    "score_training = ridge.score(X_train, y_train)\n",
    "score_test = ridge.score(X_test, y_test)\n",
    "\n",
    "print(\"Training set score: {:.2f}\".format(score_training))\n",
    "print(\"Test set score: {:.2f}\".format(score_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "QlOPNP-vrE05"
   },
   "source": [
    "The results are not good, we can assume that we need multiple features to make prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "auAYkOssi198"
   },
   "source": [
    "### Exercise 2\n",
    "\n",
    "Deploy a machine learning model that predicts the ADR as a function of whatever set of features you think are important. Explain your choices for the features and the type of ML model. If you use any, how can you handle the categorical features?\n",
    "\n",
    "How does your model perform?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 244
    },
    "colab_type": "code",
    "id": "r0hsrNZABn2J",
    "outputId": "a7e08a42-4f1f-469c-dd09-c67588428f4c"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>IsCanceled</th>\n",
       "      <th>LeadTime</th>\n",
       "      <th>ArrivalDateYear</th>\n",
       "      <th>ArrivalDateMonth</th>\n",
       "      <th>ArrivalDateWeekNumber</th>\n",
       "      <th>ArrivalDateDayOfMonth</th>\n",
       "      <th>StaysInWeekendNights</th>\n",
       "      <th>StaysInWeekNights</th>\n",
       "      <th>Adults</th>\n",
       "      <th>Children</th>\n",
       "      <th>...</th>\n",
       "      <th>DepositType</th>\n",
       "      <th>Agent</th>\n",
       "      <th>Company</th>\n",
       "      <th>DaysInWaitingList</th>\n",
       "      <th>CustomerType</th>\n",
       "      <th>ADR</th>\n",
       "      <th>RequiredCarParkingSpaces</th>\n",
       "      <th>TotalOfSpecialRequests</th>\n",
       "      <th>ReservationStatus</th>\n",
       "      <th>ReservationStatusDate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>342</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>NULL</td>\n",
       "      <td>NULL</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>-2.120120</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>2015-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>737</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>NULL</td>\n",
       "      <td>NULL</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>-2.120120</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>2015-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>NULL</td>\n",
       "      <td>NULL</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>-0.560577</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>2015-07-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>304</td>\n",
       "      <td>NULL</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>-0.560577</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>2015-07-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>2015</td>\n",
       "      <td>July</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>No Deposit</td>\n",
       "      <td>240</td>\n",
       "      <td>NULL</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>-0.082317</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Check-Out</td>\n",
       "      <td>2015-07-03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   IsCanceled  LeadTime  ArrivalDateYear ArrivalDateMonth  \\\n",
       "0           0       342             2015             July   \n",
       "1           0       737             2015             July   \n",
       "2           0         7             2015             July   \n",
       "3           0        13             2015             July   \n",
       "4           0        14             2015             July   \n",
       "\n",
       "   ArrivalDateWeekNumber  ArrivalDateDayOfMonth  StaysInWeekendNights  \\\n",
       "0                     27                      1                     0   \n",
       "1                     27                      1                     0   \n",
       "2                     27                      1                     0   \n",
       "3                     27                      1                     0   \n",
       "4                     27                      1                     0   \n",
       "\n",
       "   StaysInWeekNights  Adults  Children  ...      DepositType        Agent  \\\n",
       "0                  0       2       0.0  ...  No Deposit              NULL   \n",
       "1                  0       2       0.0  ...  No Deposit              NULL   \n",
       "2                  1       1       0.0  ...  No Deposit              NULL   \n",
       "3                  1       1       0.0  ...  No Deposit               304   \n",
       "4                  2       2       0.0  ...  No Deposit               240   \n",
       "\n",
       "       Company DaysInWaitingList CustomerType       ADR  \\\n",
       "0         NULL                 0    Transient -2.120120   \n",
       "1         NULL                 0    Transient -2.120120   \n",
       "2         NULL                 0    Transient -0.560577   \n",
       "3         NULL                 0    Transient -0.560577   \n",
       "4         NULL                 0    Transient -0.082317   \n",
       "\n",
       "   RequiredCarParkingSpaces  TotalOfSpecialRequests ReservationStatus  \\\n",
       "0                         0                       0         Check-Out   \n",
       "1                         0                       0         Check-Out   \n",
       "2                         0                       0         Check-Out   \n",
       "3                         0                       0         Check-Out   \n",
       "4                         0                       1         Check-Out   \n",
       "\n",
       "  ReservationStatusDate  \n",
       "0            2015-07-01  \n",
       "1            2015-07-01  \n",
       "2            2015-07-02  \n",
       "3            2015-07-02  \n",
       "4            2015-07-03  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "H_dataset.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "f4e9xXjlvHOY"
   },
   "source": [
    "Q: how can you handle the categorical features?\n",
    "\n",
    "Ans: I have used Label Encoder method to deal with the categorical features. It will convert the string type labels value to\n",
    "between 0 and n_classes-1.\n",
    "\n",
    "For example: if you want to convert categorical variable like \n",
    "\n",
    "[saudi arabia, Pakistan, UAE, Indonesia]\n",
    "\n",
    "the it will assign them value form 0 to number of classes etc. like \n",
    "\n",
    "[0,1,2,3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "WSe1SdHxqmWs"
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "ADR_Dateset = H_dataset['ADR'].values\n",
    "\n",
    "H_dataset.drop('ADR', axis=1, inplace=True)\n",
    "\n",
    "H_dataset_LabelEncoded=H_dataset.apply(LabelEncoder().fit_transform) # Applying label encoder\n",
    "\n",
    "feature_ADR=H_dataset_LabelEncoded.values  # ADR feature is y and all other are X\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(feature_ADR, ADR_Dateset, random_state=42) # we split the dataset between train and test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "dmQufscIdwvy"
   },
   "outputs": [],
   "source": [
    "#### Linear Regreesion for ADR prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 50
    },
    "colab_type": "code",
    "id": "KTHWS4NZUNyw",
    "outputId": "0efd1a43-cc7a-4cae-990b-e01276f6dac1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set score: 0.39\n",
      "Test set score: 0.37\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "regressor=LinearRegression() # starting the linear regression\n",
    "linear_reg=regressor.fit(X_train,y_train)# applying \n",
    "\n",
    "score_training = linear_reg.score(X_train, y_train)\n",
    "score_test = linear_reg.score(X_test, y_test)\n",
    "\n",
    "print(\"Training set score: {:.2f}\".format(score_training))\n",
    "print(\"Test set score: {:.2f}\".format(score_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ISVlFVpWvHOi"
   },
   "source": [
    "#### 2-degree Poly Regreesion for ADR prediction\n",
    "\n",
    "As test score is quit less thats why we applied Polynomial Regression to increase chances of model good fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 50
    },
    "colab_type": "code",
    "id": "Q0Eux5W26BWo",
    "outputId": "63321beb-c5dc-45db-fac7-4fbd21f69ee8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set score: 0.62\n",
      "Test set score: 0.60\n"
     ]
    }
   ],
   "source": [
    "#polynomial regression on dataset\n",
    "\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "\n",
    "poly_regressor=PolynomialFeatures(degree=2) # initilization of 2d polynomail features\n",
    "X_poly=poly_regressor.fit_transform(X_train) # Converting our features to 2 degree\n",
    "\n",
    "linear_regressor=LinearRegression()\n",
    "linear_reg_poly=linear_regressor.fit(X_poly,y_train)\n",
    "\n",
    "\n",
    "score_training = linear_reg_poly.score(X_poly, y_train)\n",
    "\n",
    "X_test_poly=poly_regressor.fit_transform(X_test)\n",
    "score_test = linear_reg_poly.score(X_test_poly, y_test)\n",
    "\n",
    "print(\"Training set score: {:.2f}\".format(score_training))\n",
    "print(\"Test set score: {:.2f}\".format(score_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ZBU-J6Imv05E"
   },
   "source": [
    "we can observe the Score has been increased from simple linear multiple regression when plynomial regression applied. But still its lower. One thing can be noticed that model is still not over fitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 50
    },
    "colab_type": "code",
    "id": "HYXuEdYLUNfE",
    "outputId": "f178e548-61f2-4107-8bdb-c41e649c2e44"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "squared_errors: 11559.441705490079\n",
      "R2 score: 0.602998079170376\n"
     ]
    }
   ],
   "source": [
    "#Sum of squared errors (Predicted vs actual)\n",
    "\n",
    "predicted=linear_reg_poly.predict(X_test_poly)\n",
    "squared_errors = (y_test - predicted) ** 2\n",
    "\n",
    "print ('squared_errors:',np.sum(squared_errors))\n",
    "\n",
    "#R^2 (coefficient of determination) regression score function.\n",
    "\n",
    "from sklearn.metrics import r2_score\n",
    "print ('R2 score:',r2_score(y_test, predicted))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "erm7X-fdi199"
   },
   "source": [
    "### Exercise 3\n",
    "\n",
    "Are you happy with the performance and efficiency of your model? What could you do to improve it? Explain and execute."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "m4s-PN0qv-XP"
   },
   "source": [
    "Performance of model is not good so we decide to apply some advance method of prediction on this dataset. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "T9tkdEEmvHO2"
   },
   "source": [
    "#### Applying Decision Tree Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 50
    },
    "colab_type": "code",
    "id": "lsD0o5BwCdv4",
    "outputId": "2317bbc9-2b80-4815-ac7c-c9e08045edda"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set score: 1.00\n",
      "Test set score: 0.77\n"
     ]
    }
   ],
   "source": [
    "\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "regressor=DecisionTreeRegressor(random_state=0)\n",
    "reg=regressor.fit(X_train,y_train)\n",
    "\n",
    "score_training = reg.score(X_train, y_train)\n",
    "score_test = reg.score(X_test, y_test)\n",
    "\n",
    "print(\"Training set score: {:.2f}\".format(score_training))\n",
    "print(\"Test set score: {:.2f}\".format(score_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 50
    },
    "colab_type": "code",
    "id": "hQw2tCnXcNwZ",
    "outputId": "fb272d97-e955-4fb3-8038-be0ae507431f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "squared_errors: 6748.862768452797\n",
      "R2 score: 0.7682144561342634\n"
     ]
    }
   ],
   "source": [
    "#Sum of squared errors (Predicted vs actual)\n",
    "\n",
    "predicted=reg.predict(X_test)\n",
    "squared_errors = (y_test - predicted) ** 2\n",
    "\n",
    "print ('squared_errors:',np.sum(squared_errors))\n",
    "\n",
    "#R^2 (coefficient of determination) regression score function.\n",
    "\n",
    "from sklearn.metrics import r2_score\n",
    "print ('R2 score:',r2_score(y_test, predicted))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "EXpDcdmxvHO-"
   },
   "source": [
    "As you can see the results are better then earlier methods."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "u7rBg4x2i19-"
   },
   "source": [
    "## Part 4: Your Turn\n",
    "\n",
    "In this final section, you are encouraged to further demonstrate your skills and knowledge using the provided dataset. Feel free to take things in whatever direction you find interesting."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "OkiS-J3hvHO_"
   },
   "source": [
    "#### Applying Random Forest Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "1GfGjGAv-ewC"
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(feature_ADR, ADR_Dateset, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 50
    },
    "colab_type": "code",
    "id": "H9VevAfle3cZ",
    "outputId": "c890943f-b93f-41ed-8075-c86e68e514ff"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set score: 0.97\n",
      "Test set score: 0.86\n"
     ]
    }
   ],
   "source": [
    "# Fitting the Random Forest Regression Model to the dataset\n",
    "\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "regressor= RandomForestRegressor(n_estimators=10,random_state=0)\n",
    "RF_regressor=regressor.fit(X_train,y_train)\n",
    "\n",
    "score_training = RF_regressor.score(X_train, y_train)\n",
    "score_test = RF_regressor.score(X_test, y_test)\n",
    "\n",
    "print(\"Training set score: {:.2f}\".format(score_training))\n",
    "print(\"Test set score: {:.2f}\".format(score_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "MPLmeSD3i7KN"
   },
   "source": [
    "Sum of squared errors (Predicted vs actual) | R^2 (coefficient of determination) regression score function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 50
    },
    "colab_type": "code",
    "id": "wJ_WWVgWe6UK",
    "outputId": "a5340f64-2256-4e13-be75-6a82fa8a62e5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "squared_errors: 4031.4742173874756\n",
      "R2 score: 0.8615414957871378\n"
     ]
    }
   ],
   "source": [
    "predicted=RF_regressor.predict(X_test)\n",
    "squared_errors = (y_test - predicted) ** 2\n",
    "\n",
    "print ('squared_errors:',np.sum(squared_errors))\n",
    "\n",
    "\n",
    "\n",
    "from sklearn.metrics import r2_score\n",
    "print ('R2 score:',r2_score(y_test, predicted))"
   ]
  },
  {
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   "execution_count": 0,
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    "colab": {},
    "colab_type": "code",
    "id": "R0aBFkIkkH6y"
   },
   "outputs": [],
   "source": []
  }
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